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 501–600 of 1,020
Deep Learning in Visual Computing and Signal Processing
Xie, D., Zhang, L., & Li, B. (2017) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2017/1320780 · Cited by 118
Ramachandran, S., George, J., Skaria, S., & Varun, V. V. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2293699 · Cited by 120
Super-resolution reconstruction of MR image with a novel residual learning network algorithm
Shi, J., Liu, Q., Wang, C., Zhang, Q., Ying, S., & Xu, H. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aab9e9 · Cited by 112
Han, D., Liu, J., Sun, Z., Cui, Y., He, Y., & Yang, Z. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105651 · Cited by 123
3D Context Enhanced Region-Based Convolutional Neural Network for End-to-End Lesion Detection
Yan, K., Bagheri, M. H., & Summers, R. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_58 · Cited by 110
Self-citationThe Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method
Han, S., Hwang, S. I., & Lee, H. J. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00230-2 · Cited by 122
Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network
Wang, B., & Wang, D. (2019) · IEEE Access · doi:10.1109/access.2019.2947510 · Cited by 119
Fuzzy Multiple-Source Transfer Learning
Jie Lu, B., Zuo, H., & Zhang, G. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2952792 · Cited by 118
Artificial intelligence in radiotherapy
Siddique, S., & Chow, J. C. L. (2020) · Reports of Practical Oncology & Radiotherapy · doi:10.1016/j.rpor.2020.03.015 · Cited by 126
Shamim, M. Z. M., Syed, S., Shiblee, M., Usman, M. J., Ali, S. J., Hussein, H. S., & Farrag, M. (2020) · The Computer Journal · doi:10.1093/comjnl/bxaa136 · Cited by 132
Machine learning in rare disease
Banerjee, J., Taroni, J., Allaway, R. J., Prasad, D. V., Guinney, J., & Greene, C. S. (2023) · Nature Methods · doi:10.1038/s41592-023-01886-z · Cited by 131
Multi-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection
Zuo, W., Zhou, F., Li, Z., & Wang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2903587 · Cited by 118
Machine Learning on Mobile: An On-device Inference App for Skin Cancer Detection
Dai, X., Spasić, ., Meyer, B., Chapman, S., & Andrès, F. (2019) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2019.8795362 · Cited by 123
Lee, J., Kim, Y., Lee, J., & Jeong, S. (2020) · Diagnostics · doi:10.3390/diagnostics10110910 · Cited by 122
Ali, S., Li, J., Pei, Y., Khurram, R., Rehman, K. U., & Mahmood, T. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09758-z · Cited by 117
“Moreover, it can be tough to have the desired accuracy for a target problem [185].”
Background(2018) · INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING · doi:10.7708/ijtte.2018.8(1).01 · Cited by 117
Computational pathology: A survey review and the way forward
Hosseini, M. S., Bejnordi, B. E., Trinh, V. Q., Chan, L., Hasan, D., Li, X., Yang, S. C., Kim, T., Zhang, H., Wu, T., et al. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100357 · Cited by 142
Wu, M., Yan, C., Liu, H., Liu, Q., & Yin, Y. (2018) · Bioscience Reports · doi:10.1042/bsr20181769 · Cited by 114
Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey
Alghamdi, H. S., Amoudi, G., Elhag, S., Saeedi, K., & Nasser, J. (2021) · IEEE Access · doi:10.1109/access.2021.3054484 · Cited by 117
Artificial Intelligence-Powered Materials Science
Bai, X., & Zhang, X. (2025) · Nano-Micro Letters · doi:10.1007/s40820-024-01634-8 · Cited by 142
“…exhibit versatile applicability across a wide spectrum of domains, encompassing organic field-effect transistors [52], micropattern manufacturing [53, 54], medical diagnosis [55], image processing [56], bio-medicine [57, 58], the field of intelligent robotics [59], electrocatalysis [60], etc.”
Artificial intelligence in diagnosis of obstructive lung disease
Das, N., Topalovic, M., & Janssens, W. (2017) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000000459 · Cited by 115
Mendel, K. R., Li, H., Sheth, D., & Giger, M. L. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.06.019 · Cited by 115
Utilizing AlexNet Deep Transfer Learning for Ear Recognition
Almisreb, A. A., Jamil, N., & Din, N. M. (2018) · International Conference on Information Retrieval and Knowledge Management (CAMP) · doi:10.1109/infrkm.2018.8464769 · Cited by 114
Deep learning enables automated scoring of liver fibrosis stages
Yu, Y., Wang, J., Ng, C. W., Ma, Y., Mo, S., Fong, E. L. S., Xing, J., Song, Z., Xie, Y., Si, K., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-34300-2 · Cited by 114
CNN-LSTM: Cascaded Framework For Brain Tumour Classification
Shahzadi, I., Tang, T. B., Meriadeau, F., & Quyyum, A. (2018) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2018.8626704 · Cited by 125
Machine Learning-Based Approach for Hardware Faults Prediction
Khalil, K., Eldash, O., Kumar, A., et al. (2020) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2020.3010743 · Cited by 114
Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network
Baghel, N., Dutta, M. K., & Bürget, R. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105750 · Cited by 114
Tandel, G. S., Tiwari, A., & Kakde, O. G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104564 · Cited by 123
Hello World Deep Learning in Medical Imaging
Lakhani, P. C., Gray, D. L., Pett, C. R., Nagy, P. G., & Shih, G. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0079-6 · Cited by 116
Xu, M., Qi, S., Yue, Y., Teng, Y., Xu, L., Yao, Y., & Qian, W. (2019) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0619-9 · Cited by 120
Hierarchical Transfer Learning for Multi-label Text Classification
Banerjee, S., Akkaya, C., Perez-Sorrosal, F., & Tsioutsiouliklis, K. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1633 · Cited by 127
Wang, S., Wang, T., Yang, L., Yang, D. M., Fujimoto, J., Yi, F., Luo, X., Yang, Y., Yao, B., Lin, S.-Y., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.10.033 · Cited by 118
Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis
Cai, L., Long, T., Dai, Y., & Huang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2976432 · Cited by 113
Zhou, H., Jin, Y., Dai, L., Zhang, M., Qiu, Y., Wang, K., Tian, J., & Zheng, J. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.108992 · Cited by 112
Salvi, M., Michielli, N., & Molinari, F. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105506 · Cited by 113
Shankar, K., & Perumal, E. (2020) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00216-6 · Cited by 113
A Novel CNN-Based Detector for Ship Detection Based on Rotatable Bounding Box in SAR Images
Yang, R., Pan, Z., Jia, X., et al. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3049851 · Cited by 113
Mujahid, M., Rehman, A. U., Alam, T., Alamri, F. S., Fati, S. M., & Saba, T. (2023) · Diagnostics · doi:10.3390/diagnostics13152489 · Cited by 125
“The authors of [35] demonstrated the effect of transfer learning on image classification and showed that fine-tuning produced better results.”
BackgroundVoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain SegmentationPreprint
Chen, H., Dou, Q., Yu, L., & Heng, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.05895 · Cited by 124
Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing
Thyreau, B., Sato, K., Fukuda, H., & Taki, Y. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.11.004 · Cited by 113
Classification of Bacterial and Viral Childhood Pneumonia Using Deep Learning in Chest Radiography
Gu, X., Pan, L., Liang, H., & Yang, R. (2018) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1145/3195588.3195597 · Cited by 113
Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning
Harsono, I. W., Liawatimena, S., & Cenggoro, T. W. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.03.013 · Cited by 126
Li, A., Xiao, F., Fan, C., & Hu, M. (2020) · Building Simulation · doi:10.1007/s12273-020-0711-5 · Cited by 120
Lakhani, P. C. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9980-7 · Cited by 111
Artificial intelligence in radiology
Filipović-Grčić, L., & Đerke, F. (2019) · Rad Hrvatske akademije znanosti i umjetnosti Medicinske znanosti · doi:10.21857/y26kec3o79 · Cited by 117
Winkler, J. K., Sies, K., Fink, C., Toberer, F., Enk, A. H., Deinlein, T., Hofmann‐Wellenhof, R., Thomas, L., Lallas, A., Blum, A., et al. (2020) · European Journal of Cancer · doi:10.1016/j.ejca.2019.11.020 · Cited by 111
Tang, L. Y. W., Coxson, H. O., Lam, S. C., Leipsic, J. A., Tam, R. C., & Sin, D. D. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30064-9 · Cited by 111
Lee, J., & Jeong, S. (2020) · Medicine · doi:10.1097/md.0000000000020787 · Cited by 111
Bai, M., Yang, X., Liu, J., Liu, J., & Yu, D. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117509 · Cited by 114
Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.017 · Cited by 110
Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment
Chen, H., Yang, B.-W., Qian, L., Meng, Y.-S., Bai, X.-H., Hong, X.-W., He, X., Jiang, M.-J., Yuan, F., Du, Q.-W., et al. (2022) · Radiology · doi:10.1148/radiol.211367 · Cited by 110
Forsberg, D., Sjöblom, E., & Sunshine, J. L. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9945-x · Cited by 113
A parasitic metric learning net for breast mass classification based on mammography
Jiao, Z., Gao, X., Wang, Y., & Li, J. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.07.008 · Cited by 109
Owais, M., Arsalan, M., Choi, J., & Park, K. R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8040462 · Cited by 110
Early Diagnosis of Alzheimer's Disease Using Deep Learning
Ji, H., Liu, Z., Yan, W. Q., & Klette, R. (2019) · International Conference on Control and Computer Vision (ICCCV) · doi:10.1145/3341016.3341024 · Cited by 117
Anagnostidis, V., Sherlock, B. E., Metz, J., Mair, P., Hollfelder, F., & Gielen, F. (2020) · Lab on a Chip · doi:10.1039/d0lc00055h · Cited by 116
Guan, S., & Loew, M. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.3.031411 · Cited by 109
Bi, L., Feng, D. D., FULHAM, M. J., & Kim, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107502 · Cited by 109
Montaha, S., Azam, S., Rafid, A. K. M. R. H., Ghosh, P., Hasan, M. Z., Jonkman, M., & Boer, F. D. (2021) · Biology · doi:10.3390/biology10121347 · Cited by 117
“, ImageNet), to medical images [51] can be a promising approach.”
BackgroundMethodInfluentialHassan, S. U., Abdulkadir, S. J., Zahid, M. S. M., & Al-Selwi, S. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109569 · Cited by 123
Alom, M. Z., Sidike, P., Hasan, M., Taha, T. M., & Asari, V. K. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6747098 · Cited by 111
Kim, K. H., Do, W., & Park, S. (2018) · Medical Physics · doi:10.1002/mp.12945 · Cited by 101
Zhong, M., LeBien, J. G., Campos‐Cerqueira, M., Dodhia, R. M., Ferres, J. L., Velev, J. P., & Aide, T. M. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107375 · Cited by 108
Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by 18F-FDG Brain PET Imaging
Pan, X., Phan, T.-L., Adel, M., Fossati, C., Gaidon, T., Wojak, J., & Guedj, É. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3022591 · Cited by 113
Wu, A. N., & Biljecki, F. (2021) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2021.104167 · Cited by 109
Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5
Xu, B., Cui, X., Ji, W., Yuan, H., & Wang, J. (2023) · Agriculture · doi:10.3390/agriculture13010124 · Cited by 111
“(2)”
Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound
Ko, S. Y., Lee, J. H., Yoon, J. H., Na, H., Hong, E., Han, K., Jung, I., Kim, E., Moon, H. J., Park, V. Y., et al. (2019) · Head & Neck · doi:10.1002/hed.25415 · Cited by 104
Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease
Kijowski, R., Liu, F., Calivá, F., & Pedoia, V. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27001 · Cited by 107
Transfer Learning With CNN for Classification of Weld Defect
Kumaresan, S., Aultrin, K. S. J., Kumar, S. S., & Anand, M. D. (2021) · IEEE Access · doi:10.1109/access.2021.3093487 · Cited by 115
Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning
Zhang, L., Soselia, D., Wang, R., & Gutierrez-Farewik, E. M. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3156786 · Cited by 120
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
Ahmed, S., Muhammod, R., Khan, Z. H., Adilina, S., Sharma, A. K., Shatabda, S., & Dehzangi, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-02703-3 · Cited by 109
Brain tumor image generation using an aggregation of GAN models with style transfer
Mukherkjee, D., Saha, P., Kaplun, D., Sinitca, A., & Sarkar, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-12646-y · Cited by 106
“(11). where z is random noise, x is the real data, and ˆ x is sampled as a linear combination of real and fake data points.”
BackgroundA novel method based on time series ensemble model for hourly photovoltaic power prediction
Xiao, Z., Huang, X., Liu, J., Li, C., & Tai, Y. (2023) · Energy · doi:10.1016/j.energy.2023.127542 · Cited by 112
The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory
Ayrey, E., & Hayes, D. J. (2018) · Remote Sensing · doi:10.3390/rs10040649 · Cited by 112
Patil, R. S., & Biradar, N. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00403-x · Cited by 105
Chaudhary, P. K., & Pachori, R. B. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102237 · Cited by 107
Distant Domain Transfer Learning for Medical Imaging
Niu, S., Liu, M., Liu, Y., Wang, J., & Song, H. H. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3051470 · Cited by 106
Deep learning for medicinal plant species classification and recognition: a systematic review
Kiflie, M. A., Sharma, D. P., & Mesfin, A. H. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1286088 · Cited by 139
Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification
Wang, Q., Zheng, Y., Yang, G., Jin, W., Chen, X., & Yin, Y. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2685586 · Cited by 105
Weng, S., Xu, X., Li, J., & Wong, S. T. C. (2017) · Journal of Biomedical Optics · doi:10.1117/1.jbo.22.10.106017 · Cited by 104
PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning
Zheng, Q., Tian, X., Yang, M., Wu, Y., & Su, H. (2019) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-019-00686-z · Cited by 110
Melanoma detection using adversarial training and deep transfer learning
Zunair, H., & Hamza, A. B. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab86d3 · Cited by 104
Mammogram breast cancer CAD systems for mass detection and classification: a review
Hassan, N. M., Hamad, S. H., & Mahar, K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12332-1 · Cited by 110
“Furthermore, other different strategies can be used to overcome the problem of the insufficient data amount such as using pre-trained models and so the pre-trained weights are transferred to initialize the network and the parameters are fine-tuned through training [72, 73].”
BackgroundA critical analysis of self-supervision, or what we can learn from a single image
Asano, Y. M., Rupprecht, C., & Vedaldi, A. (2019) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1904.13132 · Cited by 129
Davari, N., Akbarizadeh, G., & Mashhour, E. (2021) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2020.3046161 · Cited by 106
Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., & Fujita, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_12 · Cited by 101
Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders
Pesteie, M., Abolmaesumi, P., & Rohling, R. N. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2914656 · Cited by 109
Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion
Shi, C., Luo, B., He, S., Li, K., Liu, H., & Li, B. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2949355 · Cited by 104
Influence of human activity on landslide susceptibility development in the Three Gorges area
Li, Y., Wang, X., & Mao, H. (2020) · Natural Hazards · doi:10.1007/s11069-020-04264-6 · Cited by 110
Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Wang, X. Q., Chen, P., Chow, C. L., & Lau, D. (2023) · Matter · doi:10.1016/j.matt.2023.04.016 · Cited by 111
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Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France
Obst, D., de Vilmarest, J., & Goude, Y. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3067551 · Cited by 102
A deep learning study on osteosarcoma detection from histological images
Anisuzzaman, D., Barzekar, H., Tong, L., Luo, J., & Yu, Z. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102931 · Cited by 107
Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios
Candemir, S., Nguyen, X. V., Folio, L. R., & Prevedello, L. M. S. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210014 · Cited by 111
Computational biology: deep learning
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Structural crack detection using deep learning–based fully convolutional networks
Ye, X., Jin, T., & Chen, P. (2019) · Advances in Structural Engineering · doi:10.1177/1369433219836292 · Cited by 108
Gong, Y., Zhang, Y., Zhu, H., Lv, J., Cheng, Q., Zhang, H., He, Y., & Wang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2946059 · Cited by 101
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Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy
Liu, X., Li, K., Yang, R., & Geng, L. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.717039 · Cited by 108
The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis
Shen, Z., Wu, H., Chen, Z., Hu, J., Pan, J., Kong, J., & Lin, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.843735 · Cited by 103
“With the updating and progress of machine learning (ML) and deep learning (DL), AI has been applied to many fi elds, especially in the fi elds of image recognition, intelligent control, and program automation design (2, 3).”
Method
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.
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