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

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,725

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

A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

Zhou, S. K., Greenspan, H. K., Davatzikos, C. A., Duncan, J. S., van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3054390 · Cited by 1,373

Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

Gopalakrishnan, K., Khaitan, S. K., Choudhary, A., & Agrawal, A. (2017) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2017.09.110 · Cited by 981

Deep learning in medical imaging and radiation therapy

Sahiner, B., Pezeshk, A. X., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13264 · Cited by 763

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

Huynh, B. Q., Li, H., & Giger, M. L. (2016) · Journal of medical imaging · doi:10.1117/1.jmi.3.3.034501 · Cited by 554

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide

Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M. M., Greenspan, H. K., & Klang, E. (2019) · Radiology · doi:10.1148/radiol.2018180547 · Cited by 528

Deep learning for chest X-ray analysis: A survey

Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G., & Murphy, K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102125 · Cited by 565

Deep Learning in Radiology

McBee, M. P., Awan, O. A. R., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.02.018 · Cited by 512

DeepPap: Deep Convolutional Networks for Cervical Cell Classification

Zhang, L., Lu, L., Nogues, I., Summers, R. M., Liu, S., & Yao, J. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2705583 · Cited by 520

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-42294-8 · Cited by 455

Pulmonary Image Classification Based on Inception-v3 Transfer Learning Model

Wang, C., Chen, D., Hao, L., Liu, X., Zeng, Y., Chen, J., & Zhang, G. (2019) · IEEE Access · doi:10.1109/access.2019.2946000 · Cited by 402

A survey on facial emotion recognition techniques: A state-of-the-art literature review

Canal, F. Z., Müller, T. R., Matias, J. C., Scotton, G. G., de Sá, A. R., Pozzebon, E., & Sobieranski, A. C. (2021) · Information Sciences · doi:10.1016/j.ins.2021.10.005 · Cited by 358

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Hashmi, M. F., Katiyar, S., Keskar, A. G., Bokde, N. D., & Geem, Z. W. (2020) · Diagnostics · doi:10.3390/diagnostics10060417 · Cited by 372

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa, W., Nassef, M., & Badr, A. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080853 · Cited by 326

Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

Zheng, J., Lin, D., Gao, Z., Wang, S., He, M., & Fan, J. (2020) · IEEE Access · doi:10.1109/access.2020.2993536 · Cited by 329

Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs

Rajaraman, S., Candemir, S., Kim, I., Thoma, G. R., & Antani, S. (2018) · Applied Sciences · doi:10.3390/app8101715 · Cited by 328

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning

Yang, Y., Yan, L., Zhang, X., Han, Y., Nan, H., Hu, Y., Hu, B., Yan, S., Zhang, J., Cheng, D.-L., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00804 · Cited by 317

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., de Jong, P. A., Prokop, M., & Ginneken, B. V. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.001 · Cited by 289

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. (2022) · Remote Sensing · doi:10.3390/rs14143253 · Cited by 294

Large-scale retrieval for medical image analytics: A comprehensive review

Li, Z., Zhang, X., Müller, H., & Zhang, S. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.09.007 · Cited by 261

Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images

Asaoka, R., Murata, H., Hirasawa, K., Fujino, Y., Matsuura, M., Miki, A., Kanamoto, T., Ikeda, Y., Mori, K., Iwase, A., et al. (2018) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2018.10.007 · Cited by 255

Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer

Kumar, A., Singh, S. K., Saxena, S., Lakshmanan, K. S., Sangaiah, A. K., Chauhan, H., Shrivastava, S., & Singh, R. K. (2019) · Information Sciences · doi:10.1016/j.ins.2019.08.072 · Cited by 261

Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification

Hossain, S., Chakrabarty, A., Gadekallu, T. R., Alazab, M., & Piran, M. J. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3266614 · Cited by 243

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Napel, S. A., Mu, W., Jardim‐Perassi, B. V., Aerts, H. J. W. L., & Gillies, R. J. (2018) · Cancer · doi:10.1002/cncr.31630 · Cited by 235

The state of the art of deep learning models in medical science and their challenges

Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00694-1 · Cited by 222

Deep convolutional neural networks with transfer learning for automated brain image classification

Kaur, T., & Gandhi, T. K. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01069-2 · Cited by 234

Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Gopalakrishnan, K. (2018) · Data · doi:10.3390/data3030028 · Cited by 210

Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification

Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., & Wang, T. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8301998 · Cited by 210

Automated Brain Image Classification Based on VGG-16 and Transfer Learning

Kaur, T., & Gandhi, T. K. (2019) · doi:10.1109/icit48102.2019.00023 · Cited by 201

Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning

Carneiro, G., Nascimento, J. C., & Bradley, A. P. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2751523 · Cited by 198

Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural NetworksPreprint

Islam, M. T., Aowal, M. A., Minhaz, A. T., & Ashraf, K. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09850 · Cited by 247

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

Zhen, X., Chen, J., Zhong, Z., Hrycushko, B. A., Zhou, L., Jiang, S. B., Albuquerque, K., & Gu, X. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa8d09 · Cited by 191

A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

Farag, A. A., Lü, L., Roth, H. R., Liu, J., Turkbey, E. B., & Summers, R. M. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2624198 · Cited by 185

How far have we come? Artificial intelligence for chest radiograph interpretation

Kallianos, K., Mongan, J., Antani, S., Henry, T., Taylor, A., Abuya, J., & Kohli, M. (2019) · Clinical Radiology · doi:10.1016/j.crad.2018.12.015 · Cited by 173

A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors

Zhou, L., Zhang, Z., Chen, Y., Zhao, Z., Yin, X., & Jiang, H.-B. (2018) · Translational Oncology · doi:10.1016/j.tranon.2018.10.012 · Cited by 169

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Ribeiro, E. F., Uhl, A., Wimmer, G., & Häfner, M. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/6584725 · Cited by 169

Breast Mass Classification from Mammograms using Deep Convolutional Neural NetworksPreprint

Lévy, D., & Jain, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00542 · Cited by 229

Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs

Vajda, S., Karargyris, A., Jaeger, S., Santosh, K., Candemir, S., Xue, Z., Antani, S., & Thoma, G. R. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0991-9 · Cited by 164

Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2

Souid, A., Sakli, N., & Sakli, H. (2021) · Applied Sciences · doi:10.3390/app11062751 · Cited by 172

Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks

López‐Linares, K., Aranjuelo, N., Kabongo, L., Maclair, G., Lete, N., Ceresa, M., García-Familiar, A., Macía, I., & Ballester, M. Á. G. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.03.010 · Cited by 147

Marginal space deep learning: efficient architecture for volumetric image parsing

Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538802 · Cited by 142

Thorax-Net: An Attention Regularized Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography

Wang, H., Jia, H., Lü, L., & Xia, Y. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2928369 · Cited by 141

Comparison of Deep Transfer Learning Strategies for Digital Pathology

Mormont, R., Geurts, P., & Marée, R. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00303 · Cited by 134

Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions

Kalantari, A., Kamsin, A., Shamshirband, S., Gani, A., Alinejad‐Rokny, H., & Chronopoulos, A. T. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.126 · Cited by 138

Deep learning modeling using normal mammograms for predicting breast cancer risk

Arefan, D., Mohamed, A. A., Berg, W. A., Zuley, M. L., Sumkin, J. H., & Wu, S. (2019) · Medical Physics · doi:10.1002/mp.13886 · Cited by 131

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

Li, W., Li, J., Sarma, K. V., Ho, K. C., Shen, S., Knudsen, B. S., Gertych, A., & Arnold, C. W. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2875868 · Cited by 133

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Yang, L., Driscol, J., Sarigai, S., Wu, Q., Lippitt, C. D., & Morgan, M. (2022) · Sensors · doi:10.3390/s22062416 · Cited by 144

Semi-automated detection of anterior cruciate ligament injury from MRI

Štajduhar, I., Mamula, M., Miletić, D., & Ünal, G. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.006 · Cited by 136

Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features

Liu, T., Xie, S., Yu, J. S., Niu, L., & Sun, W. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952290 · Cited by 118

CRACK DAMAGE DETECTION IN UNMANNED AERIAL VEHICLE IMAGES OF CIVIL INFRASTRUCTURE USING PRE-TRAINED DEEP LEARNING MODEL

(2018) · INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING · doi:10.7708/ijtte.2018.8(1).01 · Cited by 117

Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images

Behzadi-khormouji, H., Rostami, H., Salehi, S., Derakhshande-Rishehri, T., Masoumi, M., Salemi, S., Keshavarz, A., Gholamrezanezhad, A., Assadi, M., & Batouli, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105162 · Cited by 112

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Singh, R. K., Pandey, R., & Babu, R. N. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05636-6 · Cited by 120

Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography

Mendel, K. R., Li, H., Sheth, D., & Giger, M. L. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.06.019 · Cited by 115

A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

Umer, M., Sadiq, S., Ahmad, M. I., Ullah, S., Choi, G. S., & Mehmood, A. (2020) · IEEE Access · doi:10.1109/access.2020.2994810 · Cited by 114

A Deep Learning-Based Framework for Retinal Disease Classification

Choudhary, A., Ahlawat, S., Urooj, S., Pathak, N., Lay-Ekuakille, A., & Sharma, N. (2023) · Healthcare · doi:10.3390/healthcare11020212 · Cited by 106

Survey on deep learning for pulmonary medical imaging

Ma, J., Song, Y., Tian, X., Hua, Y., Zhang, R., & Wu, J. (2019) · Frontiers of Medicine · doi:10.1007/s11684-019-0726-4 · Cited by 110

Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images

Kamal, K. C., Yin, Z., Wu, M., & Wu, Z. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-020-01820-2 · Cited by 111

Deep Learning Assisted Predict of Lung Cancer on Computed Tomography Images Using the Adaptive Hierarchical Heuristic Mathematical Model

Yu, H., Zhou, Z., & Wang, Q. (2020) · IEEE Access · doi:10.1109/access.2020.2992645 · Cited by 111

Texture Analysis and Its Applications in Biomedical Imaging: A Survey

Ghalati, M. K., Nunes, A., Ferreira, H. R. L., Serranho, P., & Bernardes, R. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3115703 · Cited by 110

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging

Hadjiiski, L. M., Cha, K., Chan, H., Drukker, K., Morra, L., Näppi, J. J., Sahiner, B., Yoshida, H., Chen, Q., Deserno, T. M., et al. (2022) · Medical Physics · doi:10.1002/mp.16188 · Cited by 107

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Cheplygina, V., de Bruijne, M., & Pluim, J. P. W. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.009 · Cited by 93

Breast Cancer Detection Using Transfer Learning in Convolutional Neural Networks

Guan, S., & Loew, M. H. (2017) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2017.8457948 · Cited by 91

Automated abnormality detection in lower extremity radiographs using deep learning

Varma, M., Lu, M., Gardner, R., Dunnmon, J., Khandwala, N., Rajpurkar, P., Long, J., Beaulieu, C. F., Shpanskaya, K. S., Fei-Fei, L., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0126-0 · Cited by 89

Advances of AI in image-based computer-aided diagnosis: A review

Yeasmin, M. N., Amin, M. A., Joti, T. J., Aung, Z., & Azim, M. A. (2024) · Array · doi:10.1016/j.array.2024.100357 · Cited by 105

Pap smear image classification using convolutional neural network

Bora, K., Chowdhury, M., Mahanta, L. B., Kundu, M. K., & Das, A. K. (2016) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3009977.3010068 · Cited by 88

Computational mammography using deep neural networks

Dubrovina, A., Kisilev, P., Ginsburg, B., Hashoul, S., & Kimmel, R. (2016) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2015.1131197 · Cited by 85

Brain Hemorrhage Diagnosis by Using Deep Learning

Phong, T. D., Duong, H. N., Nguyen, H. P. N., Trong, N. T., Nguyen, V., Hoa, T. V., & Snåšel, V. (2017) · International Conference on Machine Learning and Soft Computing (ICMLSC) · doi:10.1145/3036290.3036326 · Cited by 88

Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning

Mittal, A., Hooda, R., & Sofat, S. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2016.0526 · Cited by 83

Deep Learning Technology for Weld Defects Classification Based on Transfer Learning and Activation Features

Ajmi, C., Zapata, J., Ferchichi, S. E., Zaafouri, A., & Laabidi, K. (2020) · Advances in Materials Science and Engineering · doi:10.1155/2020/1574350 · Cited by 83

A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer's Disease using Multi-modal Neuroimaging Data

Sharma, S., & Mandal, P. K. (2022) · ACM Computing Surveys · doi:10.1145/3492865 · Cited by 91

Detection of Distal Radius Fractures Trained by a Small Set of X-Ray Images and Faster R-CNN

Yahalomi, E., Chernofsky, M. A., & Werman, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-22871-2_69 · Cited by 84

Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks

Li, S., Zhou, M., Li, Q., Hu, M., Wen, Y., Zhang, J., Lu, Y., & Chu, J. (2021) · Methods · doi:10.1016/j.ymeth.2021.04.005 · Cited by 80

IoT Based Smart Monitoring of Patients’ with Acute Heart Failure

Umer, M., Sadiq, S., Karamti, H., Karamti, W., Majeed, R., & Nappi, M. (2022) · Sensors · doi:10.3390/s22072431 · Cited by 88

Learning to Read Chest X-Ray Images from 16000+ Examples Using CNN

Dong, Y. C., Pan, Y., Zhang, J., & Xu, W. (2017) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2017.59 · Cited by 77

Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

Abidin, A. Z., Deng, B., DSouza, A. M., Nagarajan, M., Coan, P., & Wismüller, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.01.008 · Cited by 79

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

Carneiro, G., Zorron Cheng Tao Pu, L., Singh, R., & Burt, A. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101653 · Cited by 76

Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images

Hasan, M. K., Ahmed, S., Abdullah, Z. M. E., Khan, M. M., Anand, D., Singh, A. D., AlZain, M. A., & Masud, M. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/9929274 · Cited by 81

A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis

Chen, C., Seo, H., & Zhao, Y. (2021) · International Journal of Pavement Engineering · doi:10.1080/10298436.2021.1945056 · Cited by 75

Paired-unpaired Unsupervised Attention Guided GAN with transfer learning for bidirectional brain MR-CT synthesis

Abu-Srhan, A., Almallahi, I., Abushariah, M. A. M., Mahafza, W. S., & Al-Kadi, O. S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104763 · Cited by 73

Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks

Wang, J., Chen, R. J., Lu, M. Y., Baras, A. S., & Mahmood, F. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098534 · Cited by 69

Online Convolutional Neural Network-based anomaly detection and quality control for Fused Filament Fabrication process

Lyu, J., & Manoochehri, S. (2021) · Virtual and Physical Prototyping · doi:10.1080/17452759.2021.1905858 · Cited by 69

Cats or CAT scans: Transfer learning from natural or medical image source data sets?

Cheplygina, V. (2019) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2018.12.005 · Cited by 69

Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk

Biswas, M., Kuppili, V., Saba, L., Edla, D. R., Suri, H. S., Sharma, A., Cuadrado‐Godia, E., Laird, J. R., Nicolaides, A. N., & Suri, J. S. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1897-x · Cited by 72

Classification of breast lesions using cross-modal deep learning

Hadad, O., Bakalo, R., Ben‐Ari, R., Hashoul, S., & Amit, G. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950480 · Cited by 68

Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images

Rajaraman, S., Silamut, K., Hossain, M. A., Ersoy, I., Maude, R. J., Jaeger, S., Thoma, G. R., & Antani, S. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.3.034501 · Cited by 70

Applied Deep Learning in Plastic Surgery: Classifying Rhinoplasty With a Mobile App

Borsting, E. A., DeSimone, R., Ascha, M. S., & Ascha, M. (2019) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000005905 · Cited by 69

Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network

Tan, W., Liu, P., Li, X., Liu, Y., Zhou, Q., Chen, C., Gong, Z., Yin, X., & Zhang, Y. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00140-0 · Cited by 68

SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs

Jamaludin, A., Kadir, T., & Zisserman, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_20 · Cited by 63

Automated TB classification using ensemble of deep architectures

Hooda, R., Mittal, A., & Sofat, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07984-5 · Cited by 69

X-Ray Chest Image Classification by A Small-Sized Convolutional Neural Network

Kesim, E., Dokur, Z., & Ölmez, T. (2019) · Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) · doi:10.1109/ebbt.2019.8742050 · Cited by 60

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective

Mohamed, A. A., Luo, Y., Peng, H., Jankowitz, R. C., & Wu, S. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0022-2 · Cited by 63

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

Breast Tumor Segmentation in DCE-MRI With Tumor Sensitive Synthesis

Wang, S., Sun, K., Wang, L., Qu, L., Yan, F., Wang, Q., & Shen, D. G. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3129781 · Cited by 57

Computer-aided diagnosis systems: a comparative study of classical machine learning versus deep learning-based approaches

Guetari, R., Ayari, H., & Sakly, H. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01894-7 · Cited by 63

A deep learning framework for the early detection of multi-retinal diseases

Ejaz, S., Baig, R., Ashraf, Z., Alnfiai, M. M., Alnahari, M. M., & Alotaibi, R. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307317 · Cited by 56

Characterization of Lung Nodule Malignancy Using Hybrid Shape and Appearance Features

Buty, M., Xu, Z., Gao, M., Bağcı, U., Wu, A., & Mollura, D. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_77 · Cited by 56

Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †

Kim, M., Han, J. C., Hyun, S. H., Janssens, O., Hoecke, S. V., Kee, C., & Neve, W. D. (2019) · Applied Sciences · doi:10.3390/app9153064 · Cited by 56

Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms

Albahli, S., & Albattah, W. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200720 · Cited by 55

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