Chest pathology detection using deep learning with non-medical training

ImageNet 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

ImageNet: 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

A comparative study of texture measures with classification based on featured distributions

Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451

Visual categorization with bags of keypoints

Csurka, G. (2004) · Cited by 4,379

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206848 · Cited by 2,714

Convolutional networks and applications in vision

LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213

Exploring the Potential of VGG-16 Architecture for Accurate Brain Tumor Detection Using Deep Learning

Gayathri, P., Dhavileswarapu, A. V. L. S., Ibrahim, S., Paul, R., & Gupta, R. (2023) · Journal of Computers Mechanical and Management · doi:10.57159/gadl.jcmm.2.2.23056 · Cited by 65

Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs

Rajaraman, S., Thoma, G. R., Antani, S., & Candemir, S. (2019) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2512752 · Cited by 55

Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks

Islam, K. T., Wijewickrema, S. N. R., & O’Leary, S. J. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00066 · Cited by 56

Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

Guy, A., Ben‐Ari, R., Hadad, O., Monovich, E., Granot, N., & Hashoul, S. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2249981 · Cited by 54

Comparing deep learning models for population screening using chest radiography

Rajaraman, S., Antani, S., Candemir, S., Xue, Z., Thoma, G. R., Alderson, P. O., Joseph, A., & Kohli, M. D. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2293140 · Cited by 55

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images

Albahli, S. (2020) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405616666200604163954 · Cited by 53

Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm

Saba, L., Biswas, M., Suri, H. S., Višković, K., Laird, J. R., Cuadrado‐Godia, E., Nicolaides, A. N., Khanna, N. N., Viswanathan, V., & Suri, J. S. (2019) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2019.09.01 · Cited by 52

Utilizing Pretrained Deep Learning Models for Automated Pulmonary Tuberculosis Detection Using Chest Radiography

Ho, T. K. K., Gwak, J., Prakash, O., Song, J.-I., & Park, C. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14802-7_34 · Cited by 49

A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images

Wang, X., Fang, Y., Yang, S., Zhu, D., Wang, M., Zhang, J., Tong, R. K., & Han, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101914 · Cited by 50

Design and automated assembly of Planetary LEGO Brick for lunar in-situ construction

Zhou, C., Bin, T., Ding, L., Sekuła, P., Zhou, Y., & Zhang, Z. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103282 · Cited by 50

Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images

Xu, Y., Lin, L., Hu, H., Wang, D., Zhu, W., Wang, J., Han, X., & Chen, Y. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1671-9 · Cited by 47

Quality and content analysis of fundus images using deep learning

Chalakkal, R. J., Abdulla, W. H., & Thulaseedharan, S. S. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.03.019 · Cited by 52

Two-stage classification of tuberculosis culture diagnosis using convolutional neural network with transfer learning

Chang, R.-I., Chiu, Y.-H., & Lin, J.-W. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03152-x · Cited by 46

Artificial intelligence, radiomics and other horizons in body composition assessment

Attanasio, S., Forte, S. M., Restante, G., Gabelloni, M., Guglielmi, G., & Neri, E. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2020.03.10 · Cited by 47

Cross‐modality deep learning: Contouring of MRI data from annotated CT data only

Kieselmann, J. P., Fuller, C. D., Gurney‐Champion, O. J., & Oelfke, U. (2020) · Medical Physics · doi:10.1002/mp.14619 · Cited by 45

Fine-grained Patient Similarity Measuring using Deep Metric Learning

Ni, J., Liu, J., Zhang, C., Ye, D., & Ma, Z. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133022 · Cited by 44

An Ensemble Algorithm Based on Deep Learning for Tuberculosis Classification

Hernández, A., Panizo-LLedot, Á., & Camacho, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33607-3_17 · Cited by 44

Automated Gleason Grading and Gleason Pattern Region Segmentation Based on Deep Learning for Pathological Images of Prostate Cancer

Li, Y., Huang, M., Zhang, Y., Chen, J., Xu, H., Wang, G., & Feng, W. (2020) · IEEE Access · doi:10.1109/access.2020.3005180 · Cited by 43

Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network

Fitri, L. A., Haryanto, F., Arimura, H., YunHao, C., Ninomiya, K., Nakano, R., Haekal, M., Warty, Y., & Fauzi, U. (2020) · Physica Medica · doi:10.1016/j.ejmp.2020.09.007 · Cited by 45

From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning

Xu, Y., Géraud, T., & Isabelle Bloch, A. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297117 · Cited by 41

Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification

Song, Y., Li, Q., Huang, H., Feng, D. D., Chen, M., & Cai, W. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2687466 · Cited by 40

A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning

Kharazmi, P., Zheng, J., Lui, H., Wang, Z., & Lee, T. K. (2018) · Journal of Medical Systems · doi:10.1007/s10916-017-0885-2 · Cited by 38

A novel biomedical image indexing and retrieval system via deep preference learning

Pang, S., Orgun, M. A., & Yu, Z. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.003 · Cited by 40

Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles

Rajaraman, S., Kim, I., & Antani, S. (2020) · PeerJ · doi:10.7717/peerj.8693 · Cited by 39

Image Restoration for Low-Dose CT via Transfer Learning and Residual Network

Zhong, A., Li, B., Luo, N., Xu, Y. X., Zhou, L., & Zhen, X. (2020) · IEEE Access · doi:10.1109/access.2020.3002534 · Cited by 37

Automatic detection of pleural effusion in chest radiographs

Maduskar, P., Philipsen, R. H. H. M., Melendez, J., Scholten, E. T., Chanda, D. M., Ayles, H., Sá‎nchez, C. I., & van Ginneken, B. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.09.004 · Cited by 38

Domain specific convolutional neural nets for detection of architectural distortion in mammograms

Ben‐Ari, R., Akselrod-Ballin, A., Karlinsky, L., & Hashoul, S. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950581 · Cited by 38

Deep assessment process: Objective assessment process for unilateral peripheral facial paralysis via deep convolutional neural network

Guo, Z., Shen, M., Duan, L., Zhou, Y., Xiang, J., Ding, H., Chen, S., Deußen, O., & Guo, D. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950486 · Cited by 38

Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super ResolutionPreprint

Mahapatra, D., & Bozorgtabar, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.04783 · Cited by 38

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati, M., & Singh, G. K. (2024) · IEEE Access · doi:10.1109/access.2024.3360930 · Cited by 38

IMPROVED PREDICTIVE SPARSE DECOMPOSITION METHOD WITH DENSENET FOR PREDICTION OF LUNG CANCER

Mienye, I. D., Sun, Y., & Wang, Z. (2020) · International Journal of Computing · doi:10.47839/ijc.19.4.1986 · Cited by 37

Longitudinal Detection of Radiological Abnormalities with Time-Modulated LSTM

Santeramo, R., Withey, S., & Montana, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_37 · Cited by 38

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays

Avilés-Rivero, A. I., Sellars, P., Schönlieb, C., & Papadakis, N. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108274 · Cited by 39

Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification

Mobiny, A., Yuan, P., Cicalese, P. A., Moulik, S. K., Garg, N., Wu, C. C., Wong, K. A., Wong, S. T. C., He, T., & Nguyen, H. V. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3051089 · Cited by 37

Pneumonia detection in chest X-ray images using compound scaled deep learning model

Hashmi, M. F., Katiyar, S., Hashmi, A. W., & Keskar, A. G. (2021) · Automatika · doi:10.1080/00051144.2021.1973297 · Cited by 41

CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites

Ben-Cohen, A., Klang, E., Diamant, I., Rozendorn, N., Raskin, S., Konen, E., Amitai, M. M., & Greenspan, H. K. (2017) · Academic Radiology · doi:10.1016/j.acra.2017.06.008 · Cited by 35

Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning

Huynh, B. Q., Antropova, N., & Giger, M. L. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2255316 · Cited by 38

3-D Convolutional Neural Networks for Glioblastoma SegmentationPreprint

Yi, D., Zhou, M., Chen, Z., & Gevaert, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04534 · Cited by 58

Distant Viewing

Arnold, T., & Tilton, L. (2023) · The MIT Press eBooks · doi:10.7551/mitpress/14046.001.0001 · Cited by 33

Image Quality Classification for DR Screening Using Convolutional Neural Networks

Tennakoon, R., Mahapatra, D., Roy, P. K., Sedai, S., & Garnavi, R. (2016) · Ophthalmic Medical Image Analysis International Workshop (OMIA) · doi:10.17077/omia.1054 · Cited by 33

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks

Khaled, A., Han, J.-J., & Ghaleb, T. A. (2022) · IEEE Access · doi:10.1109/access.2022.3158342 · Cited by 32

On Edge Computing for Remote Pathology Consultations and Computations

Sacco, A., Esposito, F., Marchetto, G., Kolar, G. R., & Schwetye, K. E. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3007661 · Cited by 31

Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection

Mann, M., Badoni, R. P., Soni, H., Al-Shehri, M., Kaushik, A. C., & Wei, D. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00562-2 · Cited by 35

Robust Feature Spaces from Pre-Trained Deep Network Layers for Skin Lesion Classification

dos Santos, F. P., & Ponti, M. A. (2018) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2018.00031 · Cited by 31

Learning for Biomedical Information Extraction: Methodological Review of Recent AdvancesPreprint

Liu, F., Chen, J., Jagannatha, A., & Hong, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07993 · Cited by 40

Exploring texture Transfer Learning for Colonic Polyp Classification via Convolutional Neural Networks

Ribeiro, E. F., Häfner, M., Wimmer, G., Tamaki, T., Tischendorf, J. J. W., Yoshida, S., Tanaka, S., & Uhl, A. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950695 · Cited by 31

Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data

Zheng, Q., Tasian, G. E., & Yong Fan, A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363854 · Cited by 30

A Lightweight Deep Learning‐Based Pneumonia Detection Approach for Energy‐Efficient Medical Systems

Almaslukh, B. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/5556635 · Cited by 32

Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases

Rampášek, L., & Goldenberg, A. (2018) · Cell · doi:10.1016/j.cell.2018.02.013 · Cited by 30

Automatic Classification and Accuracy by Deep Learning Using CNN Methods in Lung Chest X-Ray Images

Thamilarasi, V., & Roselin, R. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1055/1/012099 · Cited by 29

Lung and Colon Cancer Detection Using a Deep AI Model

Shahadat, N., Lama, R., & Nguyen, A. (2024) · Cancers · doi:10.3390/cancers16223879 · Cited by 28

Real-Time Data Augmentation Based Transfer Learning Model for Breast Cancer Diagnosis Using Histopathological Images

Rai, R., & Sisodia, D. S. (2020) · Lecture notes in bioengineering · doi:10.1007/978-981-15-6329-4_39 · Cited by 27

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

Thammasorn, P., Chaovalitwongse, W. A., Hippe, D. S., Wootton, L. S., Ford, E. C., Spraker, M. B., Combs, S. E., Peeken, J. C., & Nyflot, M. J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3059635 · Cited by 27

Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder

Wang, C., Elazab, A., Jia, F., Wu, J., & Hu, Q. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0496-2 · Cited by 26

A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks

Sarkar, R., Hazra, A., Sadhu, K., & Ghosh, P. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-8798-2_1 · Cited by 26

Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model

Karim, M., Missen, M. M. S., Umer, M., Sadiq, S., Mohamed, A., & Ashraf, I. (2022) · Applied Sciences · doi:10.3390/app12063203 · Cited by 26

SARS n-CoV2-19 detection from chest x-ray images using deep neural networks

Pandit, M. K., & Banday, S. A. (2020) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-06-2020-0060 · Cited by 28

A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying AbnormalitiesPreprint

Zhou, B., Li, Y., & Wang, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.01257 · Cited by 25

GraphX$$^\mathbf{\small NET } -$$ Chest X-Ray Classification Under Extreme Minimal Supervision

Avilés-Rivero, A. I., Papadakis, N., Li, R., Sellars, P., Fan, Q., Tan, R. T., & Schönlieb, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_56 · Cited by 23

An improvement for the automatic classification method for ultrasound images used on CNN

Avazov, K., Abdusalomov, A. B., Mukhiddinov, M., Baratov, N., Makhmudov, F., & Cho, Y. I. (2021) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s0219691321500545 · Cited by 23

Performance Evaluation of Deep Learning Models for Image Classification Over Small Datasets: Diabetic Foot Case Study

Hernández-Guedes, A., Santana-Pérez, I., Arteaga-Marrero, N., Fabelo, H. A., Callicó, G. M., & Ruiz‐Alzola, J. (2022) · IEEE Access · doi:10.1109/access.2022.3225107 · Cited by 24

Segmentation technique for medical image processing: A survey

Merjulah, R., & Chandra, J. (2017) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici.2017.8365301 · Cited by 24

Intelligent, Secure Big Health Data Management Using Deep Learning and Blockchain Technology: An Overview

Saif, S., Biswas, S., & Chattopadhyay, S. (2019) · Studies in big data · doi:10.1007/978-3-030-33966-1_10 · Cited by 25

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Li, F., Li, S., Zhu, C., Lan, X., & Chang, H. (2017) · Remote Sensing · doi:10.3390/rs9050494 · Cited by 22

Deep learning neural networks for acrylamide identification in potato chips using transfer learning approach

Arora, M., Mangipudi, P., & Dutta, M. K. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02867-2 · Cited by 24

Learning From Synthetic InSAR With Vision Transformers: The Case of Volcanic Unrest Detection

Bountos, N. I., Michail, D., & Papoutsis, I. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3180891 · Cited by 25

Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application

Barnawi, A., Boulares, M., & Somai, R. (2023) · Bioengineering · doi:10.3390/bioengineering10030294 · Cited by 22

Bone Abnormalities Detection and Classification Using Deep Learning-Vgg16 Algorithm

Navale, V. S. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.54582 · Cited by 21

BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations

Chen, Y., Liu, Y., Wang, C., Elliott, M. S., Kwok, C. F., Peña‐Solórzano, C. A., Tian, Y., Liu, F., Frazer, H. M. L., McCarthy, D. J., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103192 · Cited by 23

An Automatic Mass Screening System for Cervical Cancer Detection Based on Convolutional Neural Network

Rehman, A.-U., Ali, N., Taj, I. A., Sajid, M., & Karimov, K. S. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/4864835 · Cited by 24

State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms

Arteaga-Marrero, N., Hernández-Guedes, A., Ortega‐Rodríguez, J., & Ruiz‐Alzola, J. (2023) · Biomedicines · doi:10.3390/biomedicines11123209 · Cited by 20

Font and Turkish Letter Recognition in Images with Deep Learning

Sevik, A., Erdoğmuş, P., & Yalein, E. (2018) · International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) · doi:10.1109/ibigdelft.2018.8625333 · Cited by 20

Identifying Carotid Plaque Composition in MRI with Convolutional Neural Networks

Dong, Y. C., Pan, Y., Zhao, X., Li, R., Yuan, C., & Xu, W. (2017) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp.2017.7947015 · Cited by 20

A zero-shot deep metric learning approach to Brain–Computer Interfaces for image retrieval

McCartney, B., Devereux, B. J., & del Rincón, J. M. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108556 · Cited by 18

An efficient ensemble method for detecting spinal curvature type using deep transfer learning and soft voting classifier

Tavana, P., Akraminia, M., Koochari, A., & Bagherifard, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119290 · Cited by 19

Chest Radiograph Pathology Categorization via Transfer Learning

Diamant, I., Bar, Y., Geva, O., Wolf, L., Zimmerman‐Moreno, G., Lieberman, S., Konen, E., & Greenspan, H. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00018-3 · Cited by 18

Robust multi-modal prostate cancer classification via feature autoencoder and dual attention

Li, B., Oka, R., Xuan, P., Yoshimura, Y., & Nakaguchi, T. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100923 · Cited by 18

Use of convolutional neural networks to predict risk of masking by mammographic density

Mainprize, J. G., Martel, A. L., Alonzo‐Proulx, O., Cleland, T., Yaffe, M. J., Jong, R. A., & Harvey, J. A. (2019) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2513063 · Cited by 17

Lung Opacity Pneumonia Detection with Improved Residual Networks

Latif, G., Anezi, F. Y. A., Sibai, F. N., & Alghazo, J. M. (2021) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-021-00656-6 · Cited by 18

Computer-Aided Detection of Lung Nodules in Chest X-Rays using Deep Convolutional Neural Networks

Uçar, M., & Uçar, E. (2019) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis.02.01.538249 · Cited by 17

Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural NetworksPreprint

Mozafari, A. S., Gomes, H. S., Leão, W., Janny, S., & Gagné, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11586 · Cited by 45

Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features

Ribeiro, E. F., Uhl, A., Wimmer, G., & Häfner, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54057-3_1 · Cited by 16

Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry

DePaoli, D. T., Tossou, P., Parent, M., Sauvageau, D., & Côté, D. C. (2019) · Scientific Reports · doi:10.1038/s41598-019-47621-7 · Cited by 16

End to End Fingerprint Verification Based on Convolutional Neural Network

Bakhshi, B., & Veisi, H. (2019) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2019.8786720 · Cited by 16

Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future

Kumar, S. N., Fred, A. L., Padmanabhan, P., Gulyás, B. Z., Kumar, H. A., & Miriam, L. R. J. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6321-8_3 · Cited by 16

LatLRR-FCNs: Latent Low-Rank Representation With Fully Convolutional Networks for Medical Image Fusion

Xu, Z., Xiang, W., Zhu, S., Zeng, R., Márquez-Chin, C., Chen, Z., Chen, X., Liu, B., & Li, J. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.615435 · Cited by 16

An efficient radiographic Image Retrieval system using Convolutional Neural Network

Chowdhury, M., Bulò, S. R., Rodrigo Moreno, A., Kundu, M. K., & Smedby, Ö. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900116 · Cited by 15

A Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection

GUAIL, A. A. A., Jin-song, G., Olouladé, B. M., & Al-Sabri, R. (2022) · Sensors · doi:10.3390/s22083049 · Cited by 15

Classification and Diagnosis of Invasive Ductal Carcinoma Using Deep Learning

Siddiqui, F., Gupta, S. K., Dubey, S., Murtuza, S., & Jain, A. (2020) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence47617.2020.9058077 · Cited by 14

Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

Zhang, S., Zhi, L., & Zhou, T. (2020) · BioMed Research International · doi:10.1155/2020/6687733 · Cited by 14

Multi-scale Multi-instance Multi-feature Joint Learning Broad Network (M3JLBN) for gastric intestinal metaplasia subtype classification

Lai, Q., Vong, C., Wong, P. K., Wang, S. T., Yan, T., Choi, I. C., & Yu, H. H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108960 · Cited by 15

Pneumothorax detection in chest radiographs using convolutional neural networks

Blumenfeld, A., Greenspan, H. K., & Konen, E. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2292540 · Cited by 14

Lung Field Segmentation in Chest X-rays: A Deformation-Tolerant Procedure Based on the Approximation of Rib Cage Seed Points

Bosdelekidis, V., & Ioakeimidis, N. S. (2020) · Applied Sciences · doi:10.3390/app10186264 · Cited by 15

A Review on Medical Image Analysis with Convolutional Neural Networks

Bir, P., & Bălaş, V. E. (2020) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/gucon48875.2020.9231203 · Cited by 15

A Computer Vision-Based Method for Classification of Red Meat Quality After Nitrosamine Appendage

Arora, M., & Mangipudi, P. (2021) · International Journal of Computational Intelligence and Applications · doi:10.1142/s146902682150005x · Cited by 13

Cascaded deep decision networks for classification of endoscopic images

Murthy, V. N., Singh, V. K., Sun, S., Bhattacharya, S., Chen, T., & Comaniciu, D. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254333 · Cited by 15

Automatic segmentation of prostate MRI based on 3D pyramid pooling Unet

Li, Y., Lin, C., Zhang, Y., Feng, S., Huang, M., & Bai, Z. (2022) · Medical Physics · doi:10.1002/mp.15895 · Cited by 15

Plaque Recognition of Carotid Ultrasound Images Based on Deep Residual Network

Ma, W., Zhou, R., Zhao, Y., Xia, Y., Fenster, A., & Ding, M. (2019) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785825 · Cited by 13

Multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images

Hashemi, S., Veisi, H., Jafarzadehpur, E., Rahmani, R., & Heshmati, Z. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02154-4 · Cited by 12

Binary classification with ambiguous training data

Otani, N., Otsubo, Y., Koike, T., & Sugiyama, M. (2020) · Machine Learning · doi:10.1007/s10994-020-05915-2 · Cited by 12

Deep learning with non-medical training used for chest pathology identification

Bar, Y., Diamant, I., Wolf, L., & Greenspan, H. K. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083124 · Cited by 307

Chest pathology identification using deep feature selection with non-medical training

Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. K. (2016) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2016.1138324 · Cited by 62

Chest Radiograph Pathology Categorization via Transfer Learning

Diamant, I., Bar, Y., Geva, O., Wolf, L., Zimmerman‐Moreno, G., Lieberman, S., Konen, E., & Greenspan, H. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00018-3 · Cited by 18

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449

Classification and Prediction of Lung Diseases According to Chest Radiography

Dumaev, R. I., & Molodyakov, S. A. (2023) · doi:10.1109/neuront58640.2023.10175838 · Cited by 3