| Predicting breast cancer by applying deep learning to linked health records and mammograms A Akselrod-Ballin, M Chorev, Y Shoshan, A Spiro, A Hazan, R Melamed, ... Radiology 292 (2), 331-342, 2019 | 192 | 2019 |
| A region based convolutional network for tumor detection and classification in breast mammography A Akselrod-Ballin, L Karlinsky, S Alpert, S Hasoul, R Ben-Ari, E Barkan Deep Learning and Data Labeling for Medical Applications: First …, 2016 | 190 | 2016 |
| Medical image description using multi-task-loss CNN P Kisilev, E Sason, E Barkan, S Hashoul Deep Learning and Data Labeling for Medical Applications: First …, 2016 | 103 | 2016 |
| Deep learning for automatic detection of abnormal findings in breast mammography A Akselrod-Ballin, L Karlinsky, A Hazan, R Bakalo, AB Horesh, Y Shoshan, ... Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 64 | 2017 |
| Artificial intelligence for reducing workload in breast cancer screening with digital breast tomosynthesis Y Shoshan, R Bakalo, F Gilboa-Solomon, V Ratner, E Barkan, ... Radiology 303 (1), 69-77, 2022 | 58 | 2022 |
| From medical image to automatic medical report generation P Kisilev, E Walach, E Barkan, B Ophir, S Alpert, SY Hashoul IBM Journal of Research and Development 59 (2/3), 2: 1-2: 7, 2015 | 58 | 2015 |
| A CNN based method for automatic mass detection and classification in mammograms A Akselrod-Ballin, L Karlinsky, S Alpert, S Hashoul, R Ben-Ari, E Barkan Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2019 | 39 | 2019 |
| Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network. Y Choukroun, R Bakalo, R Ben-Ari, A Akselrod-Ballin, E Barkan, P Kisilev VCBM, 11-19, 2017 | 32 | 2017 |
| Semantic description of medical image findings: structured learning approach. P Kisilev, E Walach, SY Hashoul, E Barkan, B Ophir, S Alpert BMVC, 171.1-171.11, 2015 | 27 | 2015 |
| Automatic generation of semantic description of visual findings in medical images P Kisilev, E Walach, E Barkan, S Hashoul US Patent 9,600,628, 2017 | 23 | 2017 |
| Method and computer program product for generating recognition error correction information N Aizenbud-Reshef, E Barkan, E Belinsky, JJ Mamou, Y Navon, B Ophir US Patent App. 11/946,847, 2009 | 22 | 2009 |
| Virtual biopsy by using artificial intelligence–based multimodal modeling of binational mammography data V Barros, T Tlusty, E Barkan, E Hexter, D Gruen, M Guindy, M Rosen-Zvi Radiology 306 (3), e220027, 2022 | 16 | 2022 |
| Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma E Barkan, C Porta, S Rabinovici-Cohen, V Tibollo, S Quaglini, M Rizzo Frontiers in Oncology 13, 1021684, 2023 | 15 | 2023 |
| Computer application analysis E Barkan, J Bnayahu, T Drory, A Ribak US Patent 8,396,964, 2013 | 15 | 2013 |
| A competition, benchmark, code, and data for using artificial intelligence to detect lesions in digital breast tomosynthesis N Konz, M Buda, H Gu, A Saha, J Yang, J Chłędowski, J Park, J Witowski, ... JAMA network open 6 (2), e230524-e230524, 2023 | 14 | 2023 |
| Siamese network for dual-view mammography mass matching S Perek, A Hazan, E Barkan, A Akselrod-Ballin Image Analysis for Moving Organ, Breast, and Thoracic Images: Third …, 2018 | 13 | 2018 |
| A generic form processing approach for large variant templates Y Navon, E Barkan, B Ophir 2009 10th International Conference on Document Analysis and Recognition, 311-315, 2009 | 13 | 2009 |
| Lessons from the first DBTex Challenge J Park, Y Shoshan, R Martí, P Gómez del Campo, V Ratner, D Khapun, ... Nature Machine Intelligence 3 (8), 735-736, 2021 | 11 | 2021 |
| Detection and segmentation of antialiased text in screen images S Gleichman, B Ophir, A Geva, M Marder, E Barkan, E Packer 2011 International Conference on Document Analysis and Recognition, 424-428, 2011 | 11 | 2011 |
| Deep Learning and Data Labeling for Medical Applications A Akselrod-Ballin, L Karlinsky, S Alpert, S Hasoul, R Ben-Ari, E Barkan, ... Athens: Springer, 2016 | 10 | 2016 |
