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Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Open accessAbstract
Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities ∼ 100% of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.
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“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.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [22]
In the citing paper
“GoogLeNet-RI-L | 0.95 | 0.85 | 0.91 | 0.69”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [22]
In the citing 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].”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [22]
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Cites
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ResultRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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BackgroundBackpropagation Applied to Handwritten Zip Code Recognition
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“It is a variation of the previously suggested “DropOut” method [29], [30].”
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How transferable are features in deep neural networks?
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Segmentation of Bone Metastasis in CT Images Based on Modified HED
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Akciğer nodül özelliklerinin tahmininde çeşitli sınıflama stratejilerinin incelenmesi
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“Akciğer nodüllerinde [16], kolonik poliplerde [17], beyin MR görüntülerinde [18], göğüs tomografisi görüntülerinde [19] nodüllerin tespiti için 2B ve 3B Evrişimsel Sinir Ağları (Convolutional Neural Networks) kullanılmıştır.”
Novel 3D Radiomic Features for Computer-Aided Polyp Detection in CT Colonography
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Self-citationDeep multi-label 3D ConvNet for breast cancer diagnosis in DBT with inversion augmentation
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Bio-medical image fusion based on phase-congruency and guided filter
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“These include the approaches based on convolutional neural networks (CNNs) [14], autoencoder (AE) [15] and restricted Boltzman machine (RBM) [16].”
MethodProgress Toward Automatic Segmentation of Soft X-ray Tomograms Using Convolutional Neural Networks
Plautz, T. E., Boudreau, R., Chen, J., Ekman, A. A., LeGros, M., McDermott, G., & Larabell, C. A. (2017) · Microscopy and Microanalysis · doi:10.1017/s143192761700558x · Cited by 5
Han, S., Zhang, T., Deng, W., Han, S., Wu, H., Jiang, B., Xie, W., Chen, Y., Deng, T., Wen, X., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-01063-6 · Cited by 5
“In addition, it can not only help to mitigate the impact of noise but also ensures that the model used focuses on GC tumors 24 .”
Huang, G., Yan, Y., Xue, J., Zhu, W., & Luó, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3427645 · Cited by 6
“The state-of-the-art meth-ods include fully-supervised methods [3], [4], [5], [45], [46], [47], [48], [49], [50], two hybrid-supervised methods (HS-SANet and HS-LSSANet), and three semi-supervised methods (SS-ND, SS-N, and SS-N + ).”
A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs
Shah, A., Al-shaibani, M. S., Ahmad, M., & Bunyan, R. (2023) · arXiv.org · doi:10.48550/arxiv.2302.09516 · Cited by 4
“Deep learning is not only limited to lesion classification as it is also widely used in medical imaging [227] like brain tumor segmentation [228], lung and candidates lesion detection [231],[232].”
BackgroundTransfer Learning-based Ensemble Deep Learning for Road Cracks Detection
Maarouf, A. A., & Hachouf, F. (2022) · International Conference on Advanced Aspects of Software Engineering · doi:10.1109/icaase56196.2022.9931581 · Cited by 4
“Furthermore, in recent years, some machine learning-based crack detection systems [17], [18] have been presented.”
BackgroundDynamic Logo Design System of Network Media Art Based on Convolutional Neural Network
Yang, Z. (2022) · Mobile Information Systems · doi:10.1155/2022/3247229 · Cited by 5
“*e Figures 4(a)–4(d) are the curves of S-type transformation function and its derivatives [23, 24].”
BackgroundRetractedA Modified U-Net Architecture for Road Surfaces Cracks Detection
Mahenge, S. F., Wambura, S. M., & Jiao, L. (2022) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3532213.3532283 · Cited by 6
“ReLU activation function is a commonly used activation function to strengthen nonlinear properties [8, 27] The gradient of ReLU will not vanish even if the input value is abnormally large.”
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“Different types of CNN architectures of CifarNet 0.15 million free parameters (Szegedy et al., 2015), AlexNet allows 1.2 million parameters with 1000 categories (Roth et al., 2015), and GoogLeNet enables the inception module to consist of 60 million parameters.”
MethodInfluentialJang, Y., & Jeon, B. (2021) · Sensors · doi:10.3390/s21186187 · Cited by 8
“As a decomposition representation, a 2.5D CNN is an alternative learning approach to reduce the computational cost compared to 3D patch-based learning tasks [6].”
MethodInfluentialFu, Y., Xue, P., Zhao, P., Li, N., Xu, Z.-D., Ji, H., Zhang, Z., Cui, W., & Dong, E.-Q. (2021) · International journal of imaging systems and technology (Print) · doi:10.1002/ima.22642 · Cited by 4
Computer Vision Based Research on PCB Recognition Using SSD Neural Network
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“H. R. Roth et al [8] applies two deep learning algorithms to handwritten font recognition.”
BackgroundImage-based bio-cad modeling: overview, scope, and challenges
Mangrulkar, A., Rane, S., & Sunnapwar, V. (2020) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1706/1/012189 · Cited by 4
Siddiqi, A., Khawaja, A., & Hashmi, A. (2020) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/muet1982.2004.07 · Cited by 4
“Various studies on liver tumor classification by semiautomatic extraction of focal liver region exist [18-20], however little work has been reported for a design that automatically classifies the entire abdominal CT scan for malignancy.”
BackgroundClassification of Chest Diseases Using Convolutional Neural Network
Ranjan, R., Singh, A. P., Rizvi, A., & Srivastava, T. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3369-3_18 · Cited by 8
Automatic Carotid Artery Detection Using Attention Layer Region-Based Convolution Neural Network
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Supine to prone colon registration and visualization based on optimal mass transport
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“Choi , Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population, Gastroenterology 129 (6) (2005) 1832–1844 .”
MethodTransfer Learning-Based Behavioural Task Decoding from Brain Activity
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Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation
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Anomaly Detection for imbalanced datasets with Deep Generative Models
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“Early cancer detection and diagnosis of abnormal anatomies, by means of Computer Tomography (CT), has been a recurrent research topic specially in the Computer Vision domain[14][5].”
Ensemble Convolution Neural Network with a Simple Voting Method for Lung Tumor Detection
Rossetto, A. M., & Zhou, W. (2017) · ACM International Conference on Bioinformatics, Computational Biology and Biomedicine · doi:10.1145/3107411.3108174 · Cited by 4
“Roth, Lu, et al. developed a method to improve upon initial computer-aided detection methods using a deep convolutional network [12].”
MethodPaediatric Frontal Chest Radiograph Screening with Fine-Tuned Convolutional Neural Networks
Gerrand, J., Williams, Q., Lunga, D., Pantanowitz, A., Madhi, S. A., & Mahomed, N. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_74 · Cited by 3
Disease-Aware Chest X-Ray Style GAN Image Generation and CatBoost Gradient Boosted Trees
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Biomedical imaging and analysis through deep learning
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“This 2.5D approach effectively integrates spatial information while maintaining the computational efficiency of 2D architectures [20].”
Pepsi, M. B. B., Anandhi, H., Karunyaharini, S., & Visali, N. (2025) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2025.05.05 · Cited by 2
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Arabi, H., & Zaidi, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106667 · Cited by 3
Cork Oak Production Estimation Using a Mask R-CNN
Guimarães, A., Valerio, M., Fidalgo, B., Salas-González, R., Pereira, C., & Mendes, M. (2022) · Energies · doi:10.3390/en15249593 · Cited by 3
Król-Całkowska, J., & Walczak, D. (2022) · Teka Komisji Prawniczej PAN Oddział w Lublinie · doi:10.32084/tekapr.2021.14.1-18 · Cited by 4
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MethodXiao, L. (2021) · Security and Communication Networks · doi:10.1155/2021/8964532 · Cited by 3
“,is is the focus of their research [6].”
BackgroundRetractedSpatio‐temporal context based recurrent visual attention model for lymph node detection
Peng, H., & Peng, Y. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22430 · Cited by 3
“On the other hand, different with the 2.5D view aggregation method 1,52 where the volumetric data is fed to the CNNs as a whole, we disentangle the 3D CT scans to the spatial (2D) and temporal (1D) domains, and successively implement the Gaussian kernel and GMM based attention process.”
MethodComputer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging
Bnouni, N. (2020) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/090103 · Cited by 4
Deep convolutional neural networks for detection and classification of tumors in mammograms
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“improves the performance of breast masses detection and classification [6, 14, 18, 19].”
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Automatic Annotation of Web Images for Domain-Specific Crack Classification
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Spine lesion analysis in 3D CT data – Reporting on research progress
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Bone Age Recognition Using Convolution Neural Network
王, 永. (2018) · Journal of Image and Signal Processing · doi:10.12677/jisp.2018.71001 · Cited by 5
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“given by the IFS algorithm.”
BackgroundMethodInfluentialMachine Learning and Artificial Intelligence in Cardiovascular Imaging
Eid, M. H., Spearman, J. V., van Assen, M., Santis, D. D., Sahbaee, P., Landreth, S. P., Jacobs, B. E., & Cecco, C. N. D. (2019) · Contemporary medical imaging · doi:10.1007/978-1-60327-237-7_68 · Cited by 2
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“The deep learning methods have been introduced to the medical imaging field with promising results on various applications, such as organ segmentations[26] and detection[27-30], tissue classification in histology and histopathology images[31-32], ultrasound standard plane selection[33],and knee cartilage segmentation[…”
MethodEsmaeilishahmirzadi, N., & Mortezapour, H. (2018) · Biomedical Research - India · doi:10.4066/biomedicalresearch.29-18-798 · Cited by 3
“In 2015, Roth et al. [12] have been using”
Staging of Hepatocellular Carcinoma Using Deep Feature in Contrast-Enhanced MR Images
Wang, Q., & Que, D. (2017) · International Conference on Computer Engineering, Information Science & Application Technology (ICCIA) · doi:10.2991/iccia-17.2017.30 · Cited by 5
“Compared with the results in [9], we applied the training method of deep learning to HCC.”
ResultDeep Learning Techniques on Texture Analysis of Chest and Breast Images
Cheng, J.-Z., Chen, C., & Shen, D. G. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00009-0 · Cited by 4
Yin, H.-C., Chang, C., Wang, Z.-W., Lin, C.-Y., Yen, Y., Tseng, Y., Chen, J.-C., & Lien, J.-J. J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127341 · Cited by 2
Time–frequency–space transformer EEG decoding for spinal cord injury
Xu, F.-Z., Liu, M., Chen, X., Yan, Y., Zhao, J., Liu, Y.-B., Zhao, J.-Q., Pang, S., Yin, S., Leng, J.-C., et al. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-024-10135-8 · Cited by 2
“The research fields combined BCI with deep learning techniques have made great progress due to the superior performance of neural networks (Roth HR et al. 2016).”
Gao, S., Guo, Z., Meng, X., Liu, L., Xiong, Y.-Z., Sun, S., & Li, H. (2024) · 2024 7th World Conference on Computing and Communication Technologies (WCCCT) · doi:10.1109/wccct60665.2024.10541474 · Cited by 2
Orthogonal integral transform for 3D shape recognition with few examples
Lin, C., Wang, P., Xiong, S., & Chen, R. (2023) · The Visual Computer · doi:10.1007/s00371-023-03030-6 · Cited by 2
“…local geometric characteristics, but fail to capture interior structural information globally, which is crucial in various 3D vision applications such as protein structure classification [7, 8, 18, 19], medical lesion detection [20, 21] and lesion recognition [22, 23] on CT volumetric images.”
Mamlook, R. E. A., Shresth, S., Gharaibeh, T., Almuflih, A., Al-Mawee, W., & Bzizi, H. (2023) · IEEE International Conference on Electro/Information Technology · doi:10.1109/eit57321.2023.10187257 · Cited by 2
“There are several CNN structural designs such as AlexNet, CiFarNet [5], GoogLeNet [6], VGG16, and VGG 19 [7,8].”
MethodMURA-objects: a radioactive bone imaging lesion detection dataset
Shao, Y.-Q., & Wang, X. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01347-1 · Cited by 2
“Reference [7] used the proposed method for automatic lymph node detection [8] and colon polyp detection [9], which improved the accuracy of target detection under radiological images.”
MethodGAN-based Privacy-Conscious Data Augmentation with Finger-Vein Images
Matsuda, Y., Miyazaki, T., & Omachi, S. (2022) · International Conference on Artificial Intelligence in Engineering and Technology · doi:10.1109/iicaiet55139.2022.9936860 · Cited by 2
“In deep learning, geometric transformations are used to effectively solve the overfitting problem [5, 6].”
MethodLiver tumor detection based on objects as points
Peng, X., & Yang, X. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac35c7 · Cited by 2
IoT‐Based Solutions for Smart Healthcare
Jain, P., Panesar, S. F., Talwar, B. F., & Sah, M. K. (2021) · Wiley eBooks · doi:10.1002/9781119792345.ch2 · Cited by 7
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Recognition Optimization of License Plate Targets Based on Improved Neural Network Model
Jiang, X., Lai, Y.-X., Song, Y., Yang, P., & Wang, S.-J. (2020) · International Journal of Intelligent Transportation Systems Research · doi:10.1007/s13177-020-00225-2 · Cited by 2
“It relies mainly on the technology of license plate recognition [8] and is an important part of intelligent transportation system [9].”
BackgroundSupriya, S., & Subaji, M. (2020) · International Journal of Intelligent Decision Technologies · doi:10.3233/idt-190083 · Cited by 2
“But the process in manually extracting these features is time consuming and complicated [10].”
Zhan, T., Zhan, F., Choi, V., Zhan, J., Deniz, S., Adrian, Ng, Gonzalez, P., Whaley, I., Garcia, D., et al. (2020) · Computing and Communication Workshop and Conference · doi:10.1109/ccwc47524.2020.9031208 · Cited by 2
“Various technological advancements, such as robotic surgery [5], nanotherapeutics [6], and computer-aided detection [7] have already been achieved in the field of biomedical engineering, but the potential of using deep learning for medical imaging applications has yet to be fully explored.”
BackgroundMaking better use of edges for sketch generation
Zhang, X., Huang, Y., Zou, Q., Guan, Q., & Liu, J. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.6.063006 · Cited by 2
Self-citationConvolutional Neural Network Cascade Based Neuron Termination Detection in 3D Image Stacks
Tan, Y., Luo, H., Wang, X., & Liu, M. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451111 · Cited by 3
“5D patch-based learning tasks [9], three”
MethodInfluentialReduction in training time of a deep learning model in detection of lesions in CT
Makkinejad, N., Tajbakhsh, N., Zarshenas, A., Khokhar, A., & Suzuki, K. (2018) · Medical Imaging · doi:10.1117/12.2293679 · Cited by 2
“Roth et al.22 used CNNs trained on augmented candidate regions of interest (ROIs) for FP reduction.”
MethodImproving Spatial Context in CNNs for Semantic Medical Image Segmentation
Mesbah, R., McCane, B., Mills, S. J., & Robins, A. V. (2017) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2017.15 · Cited by 2
“Unlike [21], [24], [27] in which multiple parallel CNN architectures are engaged in a single expensive training phase, our approach is based on only”
BackgroundBig Data analytics in medical imaging
Bagga, S., Gupta, S., & Sharma, D. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820203-6.00006-0 · Cited by 3
Wada, K., Watanabe, M., Shinchi, M., Noguchi, K., Mukoyoshi, T., Matsuyama, M., Arimura, T., & Ogino, T. (2021) · Nihon Hōshasen Gijutsu Gakkai zasshi · doi:10.6009/jjrt.2021_jsrt_77.8.787 · Cited by 2
Characterization of Errors in Deep Learning-Based Brain MRI Segmentation
Pai, A., Teng, Y.-C., Blair, J., Kallenberg, M., Dam, E. B., Sommer, S., Igel, C., & Nielsen, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00013-4 · Cited by 3
StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification
Camalan, S., Langefeld, C. D., Zinnia, A., Moberly, A., & Gurcan, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-56954-z · Cited by 1
“Classic augmentation techniques for grayscale images primarily involve affine transformations such as translation, rotation, scaling, flipping, and shearing 50 .”
Fard, S. S., Jeong, W. G., Ripalda, F. F., Sasani, H., Choi, Y., Deiva, S., Jin, G.-Y., & Ko, S.-B. (2024) · Neural computing & applications (Print) · doi:10.1007/s00521-025-11515-9 · Cited by 1
“By highlighting potential organ areas within CT images, CADe methods make screening more efficient and cost-effective [8] [9].”
MethodCharting Ethical Terrain: The Functon of Artificial Intelligence in Oral and Maxillofacial Imaging
Wadhawan, R., Yadav, A., Mittal, S. S., Mishra, P., Maiti, D., & Sengar, H. (2024) · Journal of medical and dental science research · doi:10.35629/076x-11104258 · Cited by 1
Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN
Zhi, L.-J., Duan, S., & Zhang, S.-M. (2024) · Journal of X-Ray Science and Technology · doi:10.3233/xst-240069 · Cited by 1
“Nowadays, CBMIR has become an important part of computer-aided diagnostics (CAD) [7].”
Yang, Z., Bu, Z., & Pan, Y. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/3320547 · Cited by 1
RetractedKdINet: Knowledge-driven Interpretable Network for Medical Imaging Diagnosis
Cao, Y., Cui, L.-Z., Zhang, L., Yu, F., Li, Z., Xu, Y., & Miao, C. (2022) · IEEE International Conference on Bioinformatics and Biomedicine · doi:10.1109/bibm55620.2022.9995345 · Cited by 1
“[7] proposed a multi-angle convolution neural network for the diagnosis of pulmonary tuberculosis in chest CT images.”
BackgroundPulmonary nodules recognition based on parallel cross-convolution
Hu, Y., Zhan, J., Zhou, G., Chen, A., & Li, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12908-x · Cited by 1
Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities
Küstner, T., Hepp, T., & Seith, F. (2022) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-1718-4128 · Cited by 2
“, imaging modalities or multiple scales, orientations) [74, 75].”
BackgroundThoracic Image Analysis Using Deep Learning
Wajgi, R. D., Tembhurne, J. V., & Wajgi, D. W. (2021) · Wiley eBooks · doi:10.1002/9781119818717.ch2
Ittannavar, S. (2020) · Bioscience Biotechnology Research Communications · doi:10.21786/bbrc/13.13/12 · Cited by 1
Data Augmentation for Cardiac Magnetic Resonance Image Using Evolutionary GAN
Fu, Y., Gong, M., Yang, G., & Zhou, J.-L. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-77569-8_10 · Cited by 1
An adaptive pulmonary nodule detection algorithm
Cao, K., Meng, G., Wang, Z., Liu, Y., Liu, H., & Sun, L. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200656 · Cited by 2
Lessmann, N., & van Ginneken, B. (2020) · arXiv.org · doi:10.48550/arxiv.2003.06158 · Cited by 1
Spatial-Frequency Non-local Convolutional LSTM Network for pRCC Classification
Zhao, Y. S., Liu, Y., Kan, Y., Sekuboyina, A., Waldmannstetter, D., Li, H., Hu, X., Zhao, X., Shi, K., & Menze, B. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_3 · Cited by 3
Detection of Driver Drowsiness and Distraction Using Computer Vision and Machine Learning Approaches
Okon, O. D. (2019) · doi:10.18745/th.22592 · Cited by 1
“To deal with the problem of data insufficiency, researchers now use augmented datasets [69, 70] and transfer the learning of pre-trained models [71, 72].”
BackgroundIdiopathic Interstitial Pneumonias Medical Image Detection Using Deep Learning Techniques
Sun, Z., & He, S. (2019) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3299815.3314425 · Cited by 1
“In [8], the AUC area applied to the diagnosis of pulmonary nodules can reach 0.”
MethodPredicting Lymph Node Metastasis of Lung Cancer Using Stacked Sparse Autoencoder
Wang, W., Wang, Y.-S., Wang, L., Sun, C., Cheng, X.-Y., Li, Z., Zhang, J., Yang, F., & Zhu, Y. (2018) · International Conference on the Software Process · doi:10.1109/icsp.2018.8652453 · Cited by 1
Computer-Aided Diagnosis in Chest Radiography with Deep Multi-Instance Learning
Qu, K., Chai, X., Liu, T., Zhang, Y., Leng, B., & Xiong, Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70093-9_77 · Cited by 3
Artificial neural networks applications in computer aided diagnosis
Hernández‐Rodríguez, J., Conde, M. J. R., & Fraile, F. J. C. (2016) · Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21) · doi:10.1145/3012430.3012670
“Neural networks based in Deep Learning methods [9,10] have also been used in state of the art research because they are systems with strong learning capabilities that can be trained with reasonable computer calculation resources.”
BackgroundMethodAutomated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning
Camisa, A., Montanari, G., Testa, A., Falzetti, L., Avnet, S., Baldini, N., & Notarstefano, G. (2024) · IEEE Access · doi:10.1109/access.2024.3396999 · Cited by 2
“The work in [39] instead proposes a CADe for automatic detection and classification of sclerotic lesions.”
BackgroundMethodResultInfluentialA Multi-Feature Fusion Network for Pathological Identification of Tumor Cells
Lu, Z., Zhao, J., Sun, Y., Xu, F., & Ma, X. (2022) · IEEE Access · doi:10.1109/access.2022.3160290 · Cited by 1
“Roth et al. [29] used deep learning for medical aid detection to improve the sensitivity of detecting sclerotic metastases, lymph nodes, and colon polyps in CT imaging.”
BackgroundBiswas, B., Ghosh, S. K., & Ghosh, A. (2021) · Studies in Big Data · doi:10.1007/978-3-030-75855-4_5 · Cited by 1
Alam, S. B., Hossain, M., & Kobashi, S. (2020) · International Symposium on Affective Science and Engineering · doi:10.5057/isase.2020-c000049 · Cited by 3
“CT, MRI, and mammography) [3],[4],[5].”
Hu, X., Yao, Z., Liu, F., Xie, W., Qiu, H., Dong, H., Jia, Q., Huang, M., Zhuang, J., Xu, X., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59354-4_6 · Cited by 2
Automated Tree-Level Forest Quantification Using Airborne Lidar
Hamraz, H. (2018) · UKnowledge (University of Kentucky) · doi:10.13023/etd.2018.239 · Cited by 2
“5D digital surface models (DSMs) [165-167] or multiple 2D views [168, 169] from the 3D data.”
BackgroundNaftchi-Ardebili, K., Singh, K., Pourabolghasem, R., Ghanouni, P., Popelka, G. R., & Pauly, K. B. (2023) · arXiv.org · doi:10.48550/arxiv.2308.00206
“Anonymizing, segmentation, preprocessing, and labeling of medical images cannot be easily crowdsourced due to data privacy concerns and the need for domain expertise [11, 12, 13].”
BackgroundMohammed, A. A. Q., Geng, X., Wang, J., Fateh, A., Hassan, M., & Ali, Z. (2026) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2026.115666
Lightweight and explainable CNNLSTM model for accurate lung cancer detection in thoracic CT images
Jain, S., Bajaj, K., Prabha, C., & Abdulla, S. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-71281-z
“The resultant mask is a binary image with the pixel values being 1 in the lung areas and 0 in the non-lung areas 31 .”
Metadata
- Authors
- Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M.
- Affiliations
- National Institutes of Health Clinical Center (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1170-1181
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC7340334
- OpenAlex
- W1871050032
- Cited by
- 608
- References
- 60
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 35 · 2023: 34 · 2022: 72 · 2021: 101 · 2020: 88 · 2019: 84 · 2018: 71 · 2017: 62 · 2016: 27
- FWCI
- 52.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (public-domain)
- Funders
- National Institutes of Health
- NIH Clinical Center (Z01CL040004, Z01CL040003)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- computer-aided detection, false positive reduction, convolutional neural networks, lymph node detection, colorectal polyp detection, scale transformation
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Child, Colonic Polyps, Female, Humans, Lymph Nodes, Male, Middle Aged, Radiographic Image Interpretation, Computer-Assisted, Spinal Neoplasms, Tomography, X-Ray Computed, Databases, Factual, Neural Networks, Computer, Young Adult
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