The DOI matches its Crossref record; 78 of the 104 references it cites were found in a registry or an index; none of the 71 sources its claims cite is checked for support yet.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Open accessAbstract
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.
Overview
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Written from the paper’s full text.
TL;DR
This paper empirically evaluates CNN architectures, dataset characteristics, and ImageNet transfer learning for two computer-aided detection tasks: thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. It reports state-of-the-art mediastinal LN detection and the first five-fold cross-validation ILD slice classification results, finding that transfer learning and end-to-end training generally outperform off-the-shelf CNN features.
Key findings
- For mediastinal LN detection, GoogLeNet-RI-L achieved AUC = 0.95 and 0.85 TPR/3FP, surpassing prior results of AUC = 0.92 and 0.70 TPR/3FP [22] and 0.78 TPR/3FP [36].
- Transfer learning from ImageNet consistently improved performance: GoogLeNet-TL-H versus GoogLeNet-RI-H gave 0.81 versus 0.61 TPR/3FP in mediastinum and 0.70 versus 0.48 TPR/3FP in abdomen; AlexNet-TL-H versus AlexNet-RI-H gave 0.81 versus 0.79 TPR/3FP in mediastinum and 0.69 versus 0.67 TPR/3FP in abdomen.
- For ILD slice-level five-fold CV, GoogLeNet-TL reached 0.57 accuracy versus GoogLeNet-RI 0.41, and AlexNet-TL reached 0.46 versus AlexNet-RI 0.44; patch-level ILD classification was less challenging, with five deep models in the range [0.74, 0.76] and an averaged model at 0.79.
- CNNs trained from scratch or fine-tuned from ImageNet consistently outperformed CNNs using only off-the-shelf CNN features in both LN and ILD problems.
Methods
- The study evaluated CifarNet, AlexNet, and GoogLeNet architectures (later also OverFeat and VGGNet) on two CADe tasks: thoraco-abdominal LN detection using 388 mediastinal LNs in 90 patient CT scans and 595 abdominal LNs in 86 patient CT scans, and ILD classification using 905 annotated image slices from 120 patients with six ILD labels.
- LN detection used randomly sampled 2.5D views with fields-of-view spanning 35–128 mm, rescaled to 64×64 pixels, and three-fold cross-validation with FROC AUC and TPR/3FP metrics; ILD classification used slice-level and patch-level tasks under patient-level five-fold cross-validation, with CT windowing encoded into RGB channels and data augmentation.
- Training used the Caffe framework and NVidia K40 GPUs; models trained from scratch used random Gaussian initialization for 30 epochs with mini-batch size 50, while transfer learning fine-tuned all layers except the last at a learning rate 10 times smaller than default, with the last fully connected layer randomly initialized and trained at 0.01.
Limitations
- The paper states that the ILD dataset is partially labeled, with very few CT slices labeled as normal or healthy, and that this is one of its main limitations; it also states that the dataset’s limited patient population of 120 patients may not adequately represent ILD disease CT lung imaging patterns.
- The paper states that slice-level CNN learning is restricted to only 905 CT image slices, that no existing work has reached performance requirements for a realistic clinical setting, and that the most critical bottlenecks for ILD classification are cross-dataset learning and limited patient population size.
- Evident from the reported results: ILD slice-level CV5 accuracies were low (0.41–0.57), and LOO validation produced much higher accuracies than CV5 (e.g., AlexNet-TL 0.867 versus 0.46; GoogLeNet-TL 0.902 versus 0.57), indicating sensitivity to evaluation protocol; also, for abdominal LN detection the best reported CNN result (0.70 TPR/3FP) did not surpass the prior [22] result of 0.83 TPR/3FP.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories.”
- Also stated in: § Introduction · plain
“For the first time, ILD classification results under the patient-level five-fold cross-validation protocol (CV5) are investigated and reported.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Abstract
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our experimental results suggest that different CNN architectures and dataset re-sampling protocols are critical for the LN detection tasks where the amount of labeled training data is sufficient and spatial contexts are local.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As a result, LN datasets contain more training and testing data instances (due to data auugmentation) than ILD datasets.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the publicly available ILD dataset, very few CT slices are labeled as normal or healthy.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
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In the paper
“CNN training learns to ignore them by setting very small filter weights around the corresponding regions (Fig. 13).”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: Some areas outside the lung appear in both healthy or diseased images.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There is no significant difference between two setups.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
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In the paper
“The average number of CT slices (training instances) per fold is small, as shown in Table I.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that the transfer learning strategy yields the best performance results.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Training Protocols and Transfer Learning
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Another significant finding is that CNNs trained from scratch or fine-tuned from ImageNet models consistently outperform CNNs that merely use off-the-shelf CNN features, in both the LN and ILD classification problems.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“ILD [37] is a partially labeled database; this is one of its main limitations.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
What the sources say
- Not checked yetcites [37] Depeursinge A., “ Building a reference multimedia database for interstitial lung diseases,” Comput. Med. Imaging and. Grap., vol. 36, no. 3, pp. 227– 238, 2012. 21803548
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- It may state two separate claims as one. 32
- Probably not a substantial claim. 28
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None of the sources cited for this claim have been checked yet.
In the paper
“We obtain 86% sensitivity on 3 false positives (FP) per patient, versus the prior state-of-art sensitivities of 78% [36] (stacked shallow learning) and 70% [22] (CNN), as prior state-of-the-art.”
- Also stated in: § Abstract · plain
“We achieve the state-of-the-art performance on the mediastinal LN detection”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
What the sources say
- Not checked yetcites [36] Seff A., Lu L., Barbu A., Roth H., Shin H.-C., and Summers R. M., “ Leveraging mid-level semantic boundary cues for computer-aided lymph node detection,” Proc. MICCAI, 2015. source
- Not checked yetcites [22] Roth H., “ Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imag.. PMC7340334 26441412 source
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- Also stated in: § Abstract · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They nonetheless remain less comprehensive than natural image datasets, such as ImageNet.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Fine-tuning ImageNet-trained models for ILD classification is clearly advantageous and yields early promising results, when the amount of labeled training data is highly insufficient and multi-class categorization is used, as opposed to the LN dataset's binary class categorization.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“Hence, the purely random image resampling (with respect to scale, displacement and orientation) and reformatting (the axial, coronal, and sagittal views are in any system randomly resampled coordinates) is a natural choice, which also happens to yield high CNN performance.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Thoracoabdominal Lymph Node Datasets
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In the paper
“GoogLeNet-TL-H | 0.94 | 0.81 | 0.92 | 0.70”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“GoogLeNet-TL-H yields results similar to AlexNet-TL-H's for the mediastinal LN detection, and slightly outperforms Alex-Net-H for abdominal LN detection.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Likewise, AlexNet-TL-H consistently outperforms AlexNet-RI-H, though by smaller margins (0.81 versus 0.79 TPR/3FP in mediastinum; 0.69 versus 0.67 TPR/3FP in abdomen).”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, transfer learning from ImageNet pre-trained model is consistently beneficial, as evidenced by AlexNet-TL (0.46) versus AlexNet-RI (0.44), and GoogLeNet-TL (0.57) versus GoogLeNet-RI (0.41).”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AlexNet-RI-H exhibits less severe over-fitting than GoogLeNet-RI-H.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
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None of the sources cited for this claim have been checked yet.
In the paper
“GoogLeNet-RI-L | 0.95 | 0.85 | 0.91 | 0.69”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
What the sources say
- Not checked yetcites [36] Seff A., Lu L., Barbu A., Roth H., Shin H.-C., and Summers R. M., “ Leveraging mid-level semantic boundary cues for computer-aided lymph node detection,” Proc. MICCAI, 2015. source
- Not checked yetcites [22] Roth H., “ Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imag.. PMC7340334 26441412 source
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In the paper
“Patch-CV5 | 0.76 | 0.74 | 0.76 | 0.75 | 0.76 | 0.79”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
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In the paper
“Patch-CV5 yields higher F-scores than [54] and classifies the extra consolidation (CD) class.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
What the sources say
- Not checked yetcites [54] Li Q., “ Medical image classification with convolutional neural network,” Proc. 13th Int. Conf. Control Automat. Robot. Vis., pp. 844– 848, 2014.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“The quantitative results in Table III indicate that AlexNet-TL and GoogLeNet-TL have consistently better classification accuracies than AlexNet-RI and GoogLeNet-RI, respectively.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Analysis Via CNN Learning Traces and Lulvisualization > ILD Classification
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“For example, in ILD slice-level classification, the accuracy level drastically increases from 0.46 to 0.867 using AlexNet-TL, and from 0.57 to 0.902 for GoogLeNet-TL.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Evaluation of Five CNN Models Using ILD Classification
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In the paper
“For AlexNet-RI in Fig. 11(a), the training loss significantly decreases as the number of training epochs increases, while the validation loss notably increases and the validation accuracy does not improve much before reaching a plateau.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Analysis Via CNN Learning Traces and Lulvisualization > ILD Classification
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“During the process, the validation loss, having remained at lower values throughout, achieves higher final accuracy levels than the validation loss on a similar problem with random initialization.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Analysis Via CNN Learning Traces and Lulvisualization > ILD Classification
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since LN images are more flexible than ILD images with respect to resampling and reformatting, LN datasets may be more readily augmented by such image transformations.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
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None of the sources cited for this claim have been checked yet.
In the paper
“There are currently three major techniques that successfully employ CNNs to medical image classification: 1) training the “CNN from scratch” [13]–[17]; 2) using “off-the-shelf CNN” features (without retraining the CNN) as complementary information channels to existing hand-crafted image features, for chest X-rays [10] and CT lung nodule identification [9], [12]; and 3) performing unsupervised pre-”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
What the sources say
- Not checked yetcites [13]–[17]
- Not checked yetcites [9], [12]
- Not checked yetcites [18]–[21]
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In the paper
“ImageNet is currently the largest image dataset among other standard datasets for visual recognition.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Imagenet: Large Scale Annotated Natural Image Dataset
What the sources say
- Not matchedcites [1] Deng J., “ ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 248– 255.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This significant intra-class variation poses greater challenges to any data-driven learning system that builds a classifier to fit given data and generalize to unseen data.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Imagenet: Large Scale Annotated Natural Image Dataset
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Transforming axial, coronal, and sagittal representations to RGB also facilitates transfer learning from CNN models trained on ImageNet.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Thoracoabdominal Lymph Node Datasets
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“More importantly, it also introduces a new module called “Inception”, which concatenates filters of different sizes and dimensions into a single new filter (refer to Fig. 6).”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Convolutional Neural Network Architectures
What the sources say
- Not checked yetcites [33] Szegedy C., “ Going deeper with convolutions,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2015, pp. 1– 9. source
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In the paper
“Evaluation protocols and details are critical to deriving significant empirical findings [34].”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Introduction
What the sources say
- Not checked yetcites [34] Chatfield K., Simonyan K., Vedaldi A., and Zisserman A., “ Return of the devil in the details: Delving deep into convolutional nets,” Br. Mach. Vis. Conf., 2014. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“The AlexNet architecture was published in [4], achieved significantly improved performance over the other non-deep learning methods for ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Convolutional Neural Network Architectures
What the sources say
- Not checked yetcites [4] Krizhevsky A., Sutskever I., and Hinton G. E., “ ImageNet classification with deep convolutional neural networks,” Proc. NIPS, pp. 1097– 1105, 2012. source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, the assumption that there are or must be pixel-wise spatial correlations among input channels does not apply to the CNN model representation.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Thoracoabdominal Lymph Node Datasets
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The ImageNet dataset is publicly available, and the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has become the standard benchmark for large-scale object recognition.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Imagenet: Large Scale Annotated Natural Image Dataset
What the sources say
- Not matchedcites [1] Deng J., “ ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 248– 255.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Characterizing an arbitrary CT slice against any possible ILD type, without any manual ROI (in contrast to [38], [39]), can be useful for large-scale patient screening.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“AlexNet can be fine-tuned on the PASCAL dataset to surpass the performance of the ImageNet pre-trained AlexNet, although the difference is not as significant as that between the CNN and non-CNN methods.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Training Protocols and Transfer Learning
What the sources say
- Not checked yetcites [6] Girshick R., Donahue J., Darrell T., and Malik J., “ Region-based convolutional networks for accurate object detection and semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 142– 158, Jan. 2016. 26656583 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Slice-level ILD classification is a very challenging task where CNN models need to learn from very small numbers of training examples and predict ILD labels on unseen patients.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“GoogLeNet is the current state-of-the-art CNN architecture for the ILSVRC challenge, where it achieved 5.5% top-5 classification error on the ImageNet challenge, compared to AlexNet's 15.3% top-5 classification error.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Convolutional Neural Network Architectures
What the sources say
- Not checked yetcites [33] Szegedy C., “ Going deeper with convolutions,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2015, pp. 1– 9. source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“On the other hand, the mainstream deep CNN architectures (e.g., AlexNet and GoogLeNet) contain tens of millions of free parameters to train, and thus require sufficiently large numbers of labeled medical images.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Training Protocols and Transfer Learning
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
- Rests on an assumption: Collecting and annotating large numbers of medical images still poses significant challenges.
In the paper
“ImageNet [1] has more than 1.2 million 256×256 images categorized under 1000 object class categories.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Methods > Imagenet: Large Scale Annotated Natural Image Dataset
What the sources say
- Not matchedcites [1] Deng J., “ ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 248– 255.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More importantly, these CT windowing processes do not depend on the lung segmentation, which instead is directly defined in the CT HU space.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Datasets and Related Work > Interstitial Lung Disease Dataset
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We evaluate the ILD classification task with five-fold CV on patient-level split, as it is more informative for real clinical performance than LOO.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“The experimental results in [40] are conducted with a patient-level hard split of 100 (training) and 20 (testing).”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
What the sources say
- Not checked yetcites [40] Gao M., “ Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks,” in Proc. MICCAI First Workshop Deep Learn. Med. Image Anal., 2015. PMC5881940 29623248
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“This difference lies in the fact that annotated lymph node segmentation masks are required to learn a mid-level semantic boundary detector [36], whereas CNN approaches only need LN locations for training [22].”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
What the sources say
- Not checked yetcites [36] Seff A., Lu L., Barbu A., Roth H., Shin H.-C., and Summers R. M., “ Leveraging mid-level semantic boundary cues for computer-aided lymph node detection,” Proc. MICCAI, 2015. source
- Not checked yetcites [22] Roth H., “ Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imag.. PMC7340334 26441412 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Both [38] and [39] use the evaluation protocol of “leave-one-patient-out” (LOO), which is arguably much easier and not directly comparable to 10-fold CV [54] or our Patch-CV5.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
What the sources say
- Not checked yetcites [38] Song Y., Cai W., Zhou Y., and Feng D. D., “ Feature-based image patch approximation for lung tissue classification,” IEEE Trans. Med. Imag., vol. 32, no. 4, pp. 797– 808, Apr. 2013. 23340591 source
- Not checked yetcites [39] Song Y., “ Large margin local estimate with applications to medical image classification,” IEEE Trans. Med. Imag., vol. 34, no. 6, pp. 1362– 1377, Jun. 2015. 25616009 source
- Not checked yetcites [54] Li Q., “ Medical image classification with convolutional neural network,” Proc. 13th Int. Conf. Control Automat. Robot. Vis., pp. 844– 848, 2014. source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We train and evaluate CNNs using three-fold cross-validation (folds are split into disjoint sets of patients), with the different CNN architectures described above.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“AlexNet is the first notably successful CNN architecture on the ImageNet challenge and has rekindled significant research interests on CNN.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Evaluation of Five CNN Models Using ILD Classification
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“GoogLeNet is the state-of-the-art deep model, which has outperformed other notable models, such as AlexNet, OverFeat, and VGGNet [67], [68] in various computer vision benchmarks.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Evaluation of Five CNN Models Using ILD Classification
What the sources say
- Not checked yetcites [67] Sermanet P., “ Overfeat: Integrated recognition, localization and detection using convolutional networks,” Proc. ICLR, 2014. source
- Not checked yetcites [68] Simonyan K. and Zisserman A., “ Very deep convolutional networks for large-scale image recognition,” Proc. ICLR, 2014. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“This image sampling scheme contributes to our best reported performance results in both mediastinal LN detection (in this paper) and automated pancreas segmentation [45].”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
What the sources say
- Not checked yetcites [45] Roth H., “ DeepOrgan: Multi-level deep convolutional networks for automated pancreas segmentation,” Proc. MICCAI, 2015. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The overall reported LN detection results are clinically significant, as indicated in [63].”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
What the sources say
- Not matchedcites [63] Kim L., “ Performance assessment of retroperitoneal lymph node computer-assisted detection using random forest and deep convolutional neural network learning algorithms in tandem,” in 102nd Annu. Meet. Radiol. Soc. N. Am., 2014.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“In general, patch-level ILD classification is less challenging than slice-level classification, as far more data samples can be sampled from the manually annotated ROIs (up to 100 image patches per ROI), available from [37].”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
What the sources say
- Not checked yetcites [37] Depeursinge A., “ Building a reference multimedia database for interstitial lung diseases,” Comput. Med. Imaging and. Grap., vol. 36, no. 3, pp. 227– 238, 2012. 21803548
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Recently, Gao et al. [40] have argued that a new CADe protocol on holistic classification of ILD diseases directly, using axial CT slice attenuation patterns and CNN, may be more realistic for clinical applications.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
What the sources say
- Not checked yetcites [40] Gao M., “ Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks,” in Proc. MICCAI First Workshop Deep Learn. Med. Image Anal., 2015. PMC5881940 29623248
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
In the paper
“OverFeat is described in [67] as an integrated framework for using CNN for classification, localization and detection.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Evaluation of Five CNN Models Using ILD Classification
What the sources say
- Not checked yetcites [67] Sermanet P., “ Overfeat: Integrated recognition, localization and detection using convolutional networks,” Proc. ICLR, 2014.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The main difference between our dataset preparation protocol and that from [22] is a more aggressive extraction of random views within a much larger range of FOVs.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
What the sources say
- Not checked yetcites [22] Roth H., “ Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imag.. PMC7340334 26441412
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“In abdominal LN detection, [22] obtains the best trade-off between its CNN model complexity and sampled data configuration.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
What the sources say
- Not checked yetcites [22] Roth H., “ Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE Trans. Med. Imag.. PMC7340334 26441412
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Although the ILD [37] and LTRC datasets [64] (used in [19]) were generated and annotated separately, they contain many common disease labels. For instance, the ILD disease classes emphysema (EM), ground glass (GG), fibrosis (FB), and micronodules (MN) belong to both datasets, and thus can be jointly trained/tested to form a larger and unified dataset.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
What the sources say
- Not checked yetcites [37] Depeursinge A., “ Building a reference multimedia database for interstitial lung diseases,” Comput. Med. Imaging and. Grap., vol. 36, no. 3, pp. 227– 238, 2012. 21803548 source
- Not matchedcites [64] Holmes III D., Bartholmai B., Karwoski R., Zavaletta V., and Robb R., “ The lung tissue research consortium: An extensive open database containing histological, clinical, and radiological data to study chronic lung disease,” in MICCAI Open Sci. Workshop, 2006.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Fig. 1, abdominal LNs are surrounded by many other similar looking objects. Meanwhile, mediastinal LNs are more easily distinguishable, due to the images' larger spatial contexts.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“GoogLeNet-RI-H performs poorly, as it is susceptible to over-fitting.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Thoracoabdominal Lymph Node Detection
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“A segmentation label propagation method was recently proposed [47] to provide full pixel-wise labeling of the ILD data images.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
What the sources say
- Not checked yetcites [47] Gao M., Lu L., Nogues I., Summers M. R., and Mollura D., “ Segmentation label propagation using deep convolutional neural networks and dense conditional random field,” Proc. IEEE Int. Conf. Comput. Vis., pp. 1377– 1385, 2015. source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For ILD classification, the most critical performance bottlenecks are the challenge of cross-dataset learning and the limited patient population size.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Interstitial Lung Disease Classification
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Overfeat is the winning model of the ILSVRC 2013 in detection and classification tasks.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Evaluations and Discussions > Evaluation of Five CNN Models Using ILD Classification
What the sources say
- Not checked yetcites [67] Sermanet P., “ Overfeat: Integrated recognition, localization and detection using convolutional networks,” Proc. ICLR, 2014.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The empirical evaluation, CNN model visualization, CNN performance analysis, and conclusive insights can be generalized to the design of high performance CAD systems for other medical imaging tasks.”
§ Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning > Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
The paper relies on this without support.
Fine-tuning an ImageNet pre-trained CNN model on medical image datasets had not yet been exploited at the time of the study.
“However, the fine-tuning of an ImageNet pre-trained CNN model on medical image datasets has not yet been exploited.”
The paper relies on this without support.
Obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge.
“However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge.”
The paper relies on this without support.
Some areas outside the lung appear in both healthy or diseased images.
“Some areas outside of the lung appear in both healthy or diseased images.”
The paper relies on this without support.
The low-attenuation CT window is useful for visualizing certain texture patterns of lung diseases, especially emphysema.
“The low-attenuation CT window is useful for visualizing certain texture patterns of lung diseases (especially emphysema).”
The paper relies on this without support.
Collecting and annotating large numbers of medical images still poses significant challenges.
“Collecting and annotating large numbers of medical images still poses significant challenges.”
The authors state this as an assumption.
OverFeat and VGGNet may generate quantitative performance results ranked between AlexNet's and GoogLeNet's.
“Likewise, a reasonable assumption is that OverFeat and VGGNet may generate quantitative performance results ranked between AlexNet's and GoogLeNet's.”
The authors state this as an assumption.
OverFeat's and VGGNet's performance levels fall between AlexNet's and GoogLeNet's.
“The reported results validate our assumption that OverFeat's and VGGNet's performance levels fall between AlexNet's and GoogLeNet's (this observation is consistent with the computer vision findings).”
The paper relies on this without support.
Both the ILD and ImageNet datasets use full-size images.
“Indeed, both datasets use full-size images.”
The paper relies on this without support.
CifarNet is designed for images with smaller dimensions (32×32 images), and thus is not catered to classification tasks involving 256×256 images.
“CifarNet is designed for images with smaller dimensions (32×32 images), and thus is not catered to classification tasks involving 256×256 images.”
The paper relies on this without support.
Medical datasets are often biased, in that the number of healthy samples is much larger than the number of diseased instances, or that the numbers of images per class are uneven.
“Medical datasets are often “biased”, in that the number of healthy samples is much larger than the number of diseased instances, or that the numbers of images per class are uneven.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Transfer learning from natural images has been widely adopted for medical image classification Tajbakhsh et al. (2016); Shin et al. (2016); however the application to medical image segmentation has been scarce.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Shin et al. (2016)
In the citing paper
“In general, shallow networks have been preferred in medical image analysis, when compared with very deep CNNs employed in computer vision applications [80] [81].”
From Medical image analysis using convolutional neural networks: a review (2018) · cites this paper as [80]
In the citing paper
“Healthcare applications of deep learning range from one-dimensional biosignal analysis [17] and the prediction of medical events, e.g. seizures [18] and cardiac arrests [19], to computer-aided detection [20] and diagnosis [21] supporting clinical decision making and survival analysis [22], to drug discovery [23] and as an aid in therapy selection and pharmacogenomics [24], to increased operational”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [20]
In the citing paper
“After first and second fully connected layers' dropout regularization layer [45] has been used to avoid further overfitting with a neuron dropout probability of 1-p, where p is the probability of neurons kept.”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [45]
In the citing paper
“Instead, a specially designed small network may be more suitable to the smaller-scale training datasets that are commonly encountered in medical imaging [155].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [155]
In the citing paper
“Shin et al. [62] reported that they obtained best performance with transfer learning from pre-trained model on ImageNet dataset and fine-tuning on lymph node and interstitial lung disease rather than training from scratch.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [62]
In the citing paper
“The convolutional encoder-decoder network using VGG16 shares many features with the commonly used U-Net network (29) but has unique advantages, including lower computation cost, presence of batch normalization layers to improve training efficiency, and more versatility for use as both a segmentation and classification network, which allows effective transfer learning in the current setup (30,31).”
From Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. (2018) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Five-model ensemble lists only four distinct CNNs
MinorThe purported list of five CNNs includes AlexNet-RI-H twice, leaving only four distinct model names.
“We also evaluate a simple ensemble by averaging the probability scores from five CNNs:”
“AlexNet-RI-H, AlexNet-TL-H, AlexNet-RI-H, GoogLeNet-TL-H and GoogLeNet-RI-L.”
GoogLeNet pooling-layer counts disagree
MinorThe figure caption says GoogLeNet contains three pooling layers, while the architecture description says it has two pooling layers outside its Inception layers.
“Googlenet [33] contains two convolution layers, three pooling layers, and nine inception layers.”
“Overall, GoogLeNet has two convolution layers, two pooling layers, and nine “Inception” layers.”
Table VIII labels its second task as both LN and ILD
MinorThe table caption calls the second task LN patch-level detection, but the corresponding column header calls it ILD-Patch.
“Table: Table VIII Classification accuracies for ILD slice and LN patch-level detection with “equal prior” and “biased prior”, using googlenet-TL”
“| | ILD-Slice | ILD-Patch |”
Reported mediastinal sensitivity differs from Table II
MinorThe paper reports 86% sensitivity at three false positives per patient, but Table II's best mediastinal TPR/3FP is 0.85, or 85%.
“We obtain 86% sensitivity on 3 false positives (FP) per patient”
“| Combined | 0.95 | 0.85 | 0.93 | 0.70 |”
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 2×
“GoogLeNet is the state-of-the-art deep model, which has outperformed other notable models, such as AlexNet, OverFeat, and VGGNet [67], [68] in various computer vision benchmarks.”
BackgroundMethodImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 9×
“For datadriven learning, large-scale well-annotated datasets with representative data distribution characteristics are crucial to learning more accurate or generalizable models [5], [4].”
BackgroundMethodInfluentialImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 4×
“datasets with representative data distribution characteristics are crucial to learning more accurate or generalizable models [5], [4]. Unlike previous image datasets used in computer vision, ImageNet [1] offers a very comprehensive database of more than 1.2 million categorized natural images of 1000+ classes. The CNN…”
BackgroundMethodInfluentialGradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · cited 2×
“achieved significantly improved performance over the other non-deep learning methods for ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012. This success has revived the interest in CNNs [3] in computer vision. ImageNet consists of 1.2 million 256 256 images belonging to 1000 categories. At times, the obje…”
BackgroundInfluentialDistinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · cited 2×
“Although natural images and medical images differ significantly, conventional image descriptors developed for object recognition in natural images, such as the scale-invariant feature transform (SIFT) [30] and the histogram of oriented gradients (HOG) [31], have been widely used for object detection and segmentation i…”
BackgroundGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · cited 9×
“AlexNet [4] and GoogLeNet [33] are also modified to evaluate these state-of-the-art CNN architecture from ImageNet classification task [2] to our CADe problems and datasets.”
BackgroundMethodInfluentialImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · cited 1×
“However, current FCNN approaches [65], [66] lack adequate spatial resolution in their directly output label space.”
BackgroundHistograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
Flagged first, then by citations
26 of the 104 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- seff, 2015, Leveraging mid-level semantic boundary cues for computer-aided lymph node detection, Proc MICCAI
- kim, 2014, Performance assessment of retroperitoneal lymph node computer-assisted detection using random forest and deep convolutional neural network learning algorithms in tandem, 102nd Annu Meet Radiol Soc N Am
- sermanet, 2014, Overfeat: Integrated recognition, localization and detection using convolutional networks, Proc ICLR
Cited by
Sorted by citations
Items 101–200 of 1,020
Accurate brain tumor detection using deep convolutional neural network
Khan, M. S. I., Rahman, A., Debnath, T., Karim, M. R., Nasir, M. K., Band, S. S., Mosavi, A., & Dehzangi, A. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.039 · Cited by 453
“Such knowledge helps the model to achieve high accuracy on a small dataset [23].”
BackgroundMuhammad, K., Khan, S., Ser, J. D., & de Albuquerque, V. H. C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2995800 · Cited by 411
Lin, W.-M., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., Guo, G., Xiao, M., Du, M., Qu, X., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00777 · Cited by 406
Graph embedding on biomedical networks: methods, applications and evaluations
Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S. M., Zhang, W., Zhang, P., & Sun, H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz718 · Cited by 389
A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)
Li, T., Hua, M., & Wu, X. (2020) · IEEE Access · doi:10.1109/access.2020.2971348 · Cited by 413
Thapa, C., & Camtepe, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104130 · Cited by 449
Antropova, N., Huynh, B. Q., & Giger, M. L. (2017) · Medical Physics · doi:10.1002/mp.12453 · Cited by 389
“Shin et al.31 reported that fine-tuning substantially outperformed feature extraction in the task of computer-aided detection, but they only extracted features from the final layer of AlexNet.”
Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102365 · Cited by 401
Self-Supervised ECG Representation Learning for Emotion Recognition
Sarkar, P., & Etemad, A. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3014842 · Cited by 411
IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Son, J., Bae, W., Liu, L., Wang, J., Liu, X., Gao, L., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101561 · Cited by 377
Zhang, R., Zheng, Y., Mak, T. W. C., Yu, R., Wong, S. H., Lau, J., & Poon, C. C. Y. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2635662 · Cited by 383
Classification of skin lesions using transfer learning and augmentation with Alex-net
Hosny, K. M., Kassem, M. A. A., & Foaud, M. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217293 · Cited by 389
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization
Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., & Pfeiffer, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-42557-4 · Cited by 372
Wang, X., Peng, Y., Lü, L., Lu, Z., Bagheri, M. H., & Summers, R. M. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-13969-8_18 · Cited by 381
Self-citationAl‐masni, M. A., Kim, D., & Kim, T. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105351 · Cited by 385
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Din, N. M. U., Dar, R. A., Rasool, M., & Assad, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106073 · Cited by 385
A deep learning framework for supporting the classification of breast lesions in ultrasound images
Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017) · Physics in Medicine & Biology · doi:10.1088/1361-6560/aa82ec · Cited by 358
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.378 · Cited by 354
Deep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353
Adversarial frontier stitching for remote neural network watermarking
Merrer, E. L., Pérez, P., & Trédan, G. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04434-z · Cited by 342
A survey on facial emotion recognition techniques: A state-of-the-art literature review
Canal, F. Z., Müller, T. R., Matias, J. C., Scotton, G. G., de Sá, A. R., Pozzebon, E., & Sobieranski, A. C. (2021) · Information Sciences · doi:10.1016/j.ins.2021.10.005 · Cited by 358
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks
Bi, L., Kim, J., Ahn, E., Kumar, A., FULHAM, M. J., & Feng, D. D. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2712771 · Cited by 340
Flood susceptibility mapping using convolutional neural network frameworks
Wang, Y., Fang, Z., Hong, H., & Peng, L. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124482 · Cited by 347
Jha, D., Choudhary, K. K., Tavazza, F., Liao, W., Choudhary, A., Campbell, C. E., & Agrawal, A. (2019) · Nature Communications · doi:10.1038/s41467-019-13297-w · Cited by 354
Sechopoulos, I., Teuwen, J., & Mann, R. M. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.06.002 · Cited by 376
Apostolopoulos, I. D., Aznaouridis, S. I., & Tzani, M. A. (2020) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-020-00529-4 · Cited by 341
Machine learning analysis of whole mouse brain vasculature
Todorov, M. I., Paetzold, J. C., Schoppe, O., Tetteh, G., Shit, S., Efremov, V., Todorov-Völgyi, K., Düring, M., Dichgans, M., Piraud, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0792-1 · Cited by 338
Li, Z., Wang, Y., Yu, J., Guo, Y., & Cao, W. (2017) · Scientific Reports · doi:10.1038/s41598-017-05848-2 · Cited by 346
Orhan, K., Bayrakdar, İ. Ş., Ezhov, M., Kravtsov, A., & Özyürek, T. (2020) · International Endodontic Journal · doi:10.1111/iej.13265 · Cited by 369
Roth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.01.006 · Cited by 325
Self-citationModels Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
Zhou, Z., Sodha, V., Siddiquee, M. M. R., Feng, R., Tajbakhsh, N., Gotway, M. B., & Liang, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_42 · Cited by 342
Sharif, M. I., Li, J., Khan, M. A., & Saleem, M. A. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.019 · Cited by 328
An application of cascaded 3D fully convolutional networks for medical image segmentation
Roth, H. R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., Oda, M., Fujiwara, M., Misawa, K., & Mori, K. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.03.001 · Cited by 330
Self-citationAubreville, M., Knipfer, C., Oetter, N., Jaremenko, C., Rodner, E., Denzler, J., Bohr, C., Neumann, H., Stelzle, F., & Maier, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-12320-8 · Cited by 345
Zhou, Z., Sodha, V., Pang, J., Gotway, M. B., & Liang, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101840 · Cited by 375
SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network
Jin, Y., Dou, Q., Chen, H., Yu, L., Qin, J., Fu, C.-W., & Heng, P.-A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2787657 · Cited by 316
Qi, J., Liu, X., Liu, K., Xu, F., Hui, G., Tian, X., Li, M., Bao, Z., & Li, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106780 · Cited by 335
Deep features for breast cancer histopathological image classification
Spanhol, F. A., Oliveira, L. S., Cavalin, P., Petitjean, C., & Heutte, L. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122889 · Cited by 340
Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
Yu, J., Deng, Y., Liu, T., Zhou, J., Jia, X., Xiao, T., Zhou, S., Li, J., Guo, Y., Wang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18497-3 · Cited by 329
Medical image analysis based on deep learning approach
Puttagunta, M. K., & Ravi, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10707-4 · Cited by 349
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image SegmentationPreprint
Tajbakhsh, N., Jeyaseelan, L., Li, Q., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10454 · Cited by 324
Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., & Kijowski, R. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26841 · Cited by 319
Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning
Yang, Y., Yan, L., Zhang, X., Han, Y., Nan, H., Hu, Y., Hu, B., Yan, S., Zhang, J., Cheng, D.-L., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00804 · Cited by 317
Moon, W. K., Lee, Y., Ke, H.-H., Lee, S. H., Huang, C., & Chang, R. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105361 · Cited by 329
Shen, L., Zhao, W., & Xing, L. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0466-4 · Cited by 328
Literature review: efficient deep neural networks techniques for medical image analysis
Abdou, M. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06960-9 · Cited by 341
“Another study was presented by Shin in [26] in the same area of lymph nodes cancer detection from CT images, where a CNN was used.”
MethodA survey on incorporating domain knowledge into deep learning for medical image analysis
Xie, X., Niu, J., Liu, X., Chen, Z., Tang, S., & Yu, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101985 · Cited by 331
Machine Learning–Based Radiomics for Molecular Subtyping of Gliomas
Lu, C., Hsu, F., Hsieh, K. L., Kao, Y. J., Cheng, S., Hsu, J. B., Tsai, P., Chen, R., Huang, C., Yen, Y., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3445 · Cited by 306
Seasonal Crops Disease Prediction and Classification Using Deep Convolutional Encoder Network
Khamparia, A., Saini, G., Gupta, D. K., Khanna, A. K., Tiwari, S., & de Albuquerque, V. H. C. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01041-0 · Cited by 308
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302
A Review of Deep Learning on Medical Image Analysis
Wang, J., Zhu, H., Wang, S., & Zhang, Y. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01672-7 · Cited by 318
Aoki, T., Yamada, A., Aoyama, K., Saito, H., Tsuboi, A., Nakada, A., Niikura, R., Fujishiro, M., Oka, S., Ishihara, S., et al. (2018) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2018.10.027 · Cited by 307
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303
Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification
Vogado, L. H. S., Veras, R. D. M. S., Araújo, F. H. D., Silva, R., & Aires, K. R. T. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.04.024 · Cited by 310
Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
Ünver, H. M., & Ayan, E. (2019) · Diagnostics · doi:10.3390/diagnostics9030072 · Cited by 313
Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble
Rasti, R., Rabbani, H., Mehridehnavi, A., & Hajizadeh, F. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2780115 · Cited by 299
Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis
Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., & Mougiakakou, S. G. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636929 · Cited by 303
Multi-Scale Metric Learning for Few-Shot Learning
Jiang, W. R., Huang, K., Geng, J., & Deng, X. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2995754 · Cited by 301
Liu, F., Zhou, Z., Samsonov, A., Blankenbaker, D., Larison, W., Kanarek, A., Lian, K., Kambhampati, S., & Kijowski, R. (2018) · Radiology · doi:10.1148/radiol.2018172986 · Cited by 297
Review on deep learning applications in frequency analysis and control of modern power system
Zhang, Y., Shi, X., Zhang, H., Cao, Y., & Terzija, V. V. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107744 · Cited by 304
Pre-trained convolutional neural networks as feature extractors for tuberculosis detection
Lopes, U. K., & Valiati, J. F. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.001 · Cited by 294
Byra, M., Styczyński, G., Szmigielski, C. A., Kalinowski, P., Michałowski, Ł., Paluszkiewicz, R., Ziarkiewicz‐Wróblewska, B., Zieniewicz, K., Sobieraj, P., & Nowicki, A. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1843-2 · Cited by 314
A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images
Mehmood, A., Yang, S., Feng, Z., Wang, M., Smadi, A. A., Khan, R., Maqsood, M., & Yaqub, M. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.01.002 · Cited by 305
Artificial Intelligence in Lung Cancer Pathology Image Analysis
Wang, S., Yang, D. M., Rong, R., Zhan, X., Fujimoto, J., Liu, H., Minna, J. D., Wistuba, I. I., Xie, Y., & Xiao, G. (2019) · Cancers · doi:10.3390/cancers11111673 · Cited by 296
A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
Islam, M. M., Karray, F. O., Alhajj, R., & Zeng, J. (2021) · IEEE Access · doi:10.1109/access.2021.3058537 · Cited by 299
Pi, Y., Nath, N. D., & Behzadan, A. H. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.101009 · Cited by 301
Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning
Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016) · Scientific Reports · doi:10.1038/srep27327 · Cited by 291
Samala, R. K., Chan, H., Hadjiiski, L. M., Helvie, M. A., Wei, J., & Kenny, H. (2016) · Medical Physics · doi:10.1118/1.4967345 · Cited by 288
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019) · IEEE Access · doi:10.1109/access.2019.2929365 · Cited by 301
Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms
Lin, L., Li, W., Bi, H., & Qin, L. (2021) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2021.3049404 · Cited by 285
“Finally, it would be interesting to extend our approach to other trajectory data sets through transfer learning [35].”
BackgroundBrain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines
Pashaei, A., Sajedi, H., & Jazayeri, N. (2018) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2018.8566571 · Cited by 320
Deep learning in medical image analysis: A third eye for doctors
Fourcade, A., & Khonsari, R. H. (2019) · Journal of Stomatology Oral and Maxillofacial Surgery · doi:10.1016/j.jormas.2019.06.002 · Cited by 293
Xie, Y., Zhang, J., Xia, Y., FULHAM, M. J., & Zhang, Y. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.10.005 · Cited by 290
Banerjee, I., Yuan, L., Chen, M. C., Hasan, S. A., Langlotz, C. P., Moradzadeh, N., Chapman, B. E., Amrhein, T. J., Mong, D. A., Rubin, D. L., et al. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.11.004 · Cited by 292
Liang, G., Hong, H., Xie, W., & Zheng, L. (2018) · IEEE Access · doi:10.1109/access.2018.2846685 · Cited by 291
An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network
Jiang, H., Ma, H., Qian, W., Gao, M., & Li, Y. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2725903 · Cited by 284
Self-citationFace Mask Wearing Detection Algorithm Based on Improved YOLO-v4
Yu, J., & Zhang, W. (2021) · Sensors · doi:10.3390/s21093263 · Cited by 295
A survey of data fusion in smart city applications
Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U.-X. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.05.004 · Cited by 320
A review on medical imaging synthesis using deep learning and its clinical applications
Wang, T., Lei, Y., Fu, Y., Wynne, J. F., Curran, W. J., Liu, T., & Yang, X. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13121 · Cited by 305
Breast cancer histology images classification: Training from scratch or transfer learning?
Shallu, & Mehra, R. (2018) · ICT Express · doi:10.1016/j.icte.2018.10.007 · Cited by 290
Sahlol, A. T., Kollmannsberger, P., & Ewees, A. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-59215-9 · Cited by 284
DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Goyal, M., Reeves, N. D., Davison, A. K., Rajbhandari, S. M., Spragg, J., & Yap, M. H. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2866254 · Cited by 293
Deep learning in mammography and breast histology, an overview and future trends
Hamidinekoo, A., Denton, E., Rampun, A., Honnor, K., & Reyer Zwiggelaar, A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.03.006 · Cited by 283
Nguyen, L. D., Lin, D., Lin, Z., & Cao, J. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351550 · Cited by 306
Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling
Hirra, I., Ahmad, M., Hussain, A., Ashraf, M. U., Saeed, I. A., Qadri, S. F., Alghamdi, A. M., & Alfakeeh, A. S. (2021) · IEEE Access · doi:10.1109/access.2021.3056516 · Cited by 277
Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era
Mitsala, A., Tsalikidis, C., Pitiakoudis, M. S., Simopoulos, C. E., & Tsaroucha, A. K. (2021) · Current Oncology · doi:10.3390/curroncol28030149 · Cited by 294
Urakawa, T., Tanaka, Y., Goto, S., Matsuzawa, H., Watanabe, K., & Endo, N. (2018) · Skeletal Radiology · doi:10.1007/s00256-018-3016-3 · Cited by 271
Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review
Gardezi, S. J. S., Elazab, A., Lei, B., & Wang, T. (2019) · Journal of Medical Internet Research · doi:10.2196/14464 · Cited by 283
A deep learning method for classifying mammographic breast density categories
Mohamed, A. A., Berg, W. A., Peng, H., Luo, Y., Jankowitz, R. C., & Wu, S. (2017) · Medical Physics · doi:10.1002/mp.12683 · Cited by 269
DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG
Seal, A., Bajpai, R., Agnihotri, J., Yazidi, A., Herrera‐Viedma, E., & Krejcar, O. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3053999 · Cited by 279
Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B. J., Cabarcos, A. H., Santos, A., Sánchez, C. I., & Ledesma‐Carbayo, M. J. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.000892 · Cited by 270
Large-scale retrieval for medical image analytics: A comprehensive review
Li, Z., Zhang, X., Müller, H., & Zhang, S. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.09.007 · Cited by 261
Madani, A., Ong, J. R., Tibrewal, A., & Mofrad, M. R. K. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0065-x · Cited by 250
Özdemir, O., Russell, R. L., & Berlin, A. A. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2947595 · Cited by 274
Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taïar, R., Hancock, P. A., & Aljuaid, A. M. (2021) · Brain Sciences · doi:10.3390/brainsci11111525 · Cited by 295
“Although DL models can effectively solve the EEG signal classification tasks, transfer learning strategy from one model to another accelerates training time and yields the best performance results [291].”
BackgroundTowards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., & R. Oleiwi, S. (2020) · Applied Sciences · doi:10.3390/app10134523 · Cited by 258
Yu, C., Han, R., Song, M., Liu, C., & Chang, C. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2983224 · Cited by 261
Asaoka, R., Murata, H., Hirasawa, K., Fujino, Y., Matsuura, M., Miki, A., Kanamoto, T., Ikeda, Y., Mori, K., Iwase, A., et al. (2018) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2018.10.007 · Cited by 255
Tandel, G. S., Balestrieri, A., Jujaray, T., Khanna, N. N., Saba, L., & Suri, J. S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103804 · Cited by 266
A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays
Aziz, F., Ul-Haq, A., Ahmad, S., Mahmoud, Y., Jalal, M., & Ali, U. (2020) · IEEE Access · doi:10.1109/access.2020.2977116 · Cited by 257
Metadata
- Authors
- Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Shin, Hoo-Chang
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1285-1298
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC4890616
- OpenAlex
- W2253429366
- Cited by
- 5,759
- References
- 104
- Citations by year
- 2026: 279 · 2025: 480 · 2024: 572 · 2023: 614 · 2022: 725 · 2021: 872 · 2020: 808 · 2019: 712 · 2018: 426 · 2017: 223 · 2016: 44
- FWCI
- 385.74
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health
- NIH Clinical Center (Z01CL040004)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- computer-aided detection, deep convolutional neural networks, transfer learning, CNN architecture comparison, medical image classification, ImageNet pre-training, fine-tuning, lymph node detection, interstitial lung disease classification, computed tomography
- MeSH terms
- Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Lymph Nodes, Reproducibility of Results, Databases, Factual, Neural Networks, Computer, Lung Diseases, Interstitial
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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