SSLP: Spatial Guided Self-supervised Learning on Pathological Images

Momentum Contrast for Unsupervised Visual Representation Learning

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works

A simple framework for contrastive learning of visual representations

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works

Representation Learning with Contrastive Predictive Coding

Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · see citing works

Tumour heterogeneity and resistance to cancer therapies

Dagogo-Jack, I., & Shaw, A. T. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.166 · Cited by 3,846 · see citing works

Unsupervised Feature Learning via Non-parametric Instance Discrimination

Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664 · see citing works

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09882 · Cited by 2,499 · see citing works

Unsupervised Representation Learning by Predicting Image Rotations

Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv · doi:10.48550/arxiv.1803.07728 · Cited by 2,477 · see citing works

Big data and machine learning algorithms for health-care delivery

Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · see citing works

Digital pathology and artificial intelligence

Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works

Tumors as Organs: Complex Tissues that Interface with the Entire Organism

Egeblad, M., Nakasone, E. S., & Werb, Z. (2010) · Developmental Cell · doi:10.1016/j.devcel.2010.05.012 · Cited by 1,230 · see citing works

Predicting cancer outcomes from histology and genomics using convolutional networks

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084 · see citing works

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 739 · see citing works

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30739-9 · Cited by 705 · see citing works

Multi-class texture analysis in colorectal cancer histology

Kather, J. N., Weis, C.-A., Bianconi, F., Melchers, S. M., Schad, L. R., Gaiser, T., Marx, A., & Zöllner, F. G. (2016) · Scientific Reports · doi:10.1038/srep27988 · Cited by 578 · see citing works

Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Gräf, S., Chin, S.-F., Schwarz, R. F., Curtis, C., Dunning, M. J., Bardwell, H., et al. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3004330 · Cited by 426 · see citing works

Local Aggregation for Unsupervised Learning of Visual Embeddings

Zhuang, C., Zhai, A., & Yamins, D. L. K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00610 · Cited by 422 · see citing works

Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer

Golden, J. A. (2017) · JAMA · doi:10.1001/jama.2017.14580 · Cited by 297 · see citing works

100,000 Histological Images Of Human Colorectal Cancer And Healthy Tissue

Kather, J. N., Halama, N., & Marx, A. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1214456 · Cited by 180 · see citing works

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic studyPreprint

Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J. A. W. M., van de Kaa, C. A. H. ‐., & Litjens, G. J. S. (2020) · arXiv (Cornell University) · Cited by 106 · see citing works

Instance-Aware Self-supervised Learning for Nuclei Segmentation

Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., & Zheng, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_33 · Cited by 68 · see citing works

Are all negatives created equal in contrastive instance discrimination?Preprint

Cai, T., Frankle, J., Schwab, D. J., & Morcos, A. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06682 · Cited by 58 · see citing works

Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

Abbet, C., Zlobec, I., Bozorgtabar, B., & Thiran, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_46 · Cited by 55 · see citing works

Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding (Conference Presentation)

Lu, M. Y., Chen, R. J., & Mahmood, F. (2020) · SPIE Medical Imaging: Digital Pathology · doi:10.1117/12.2549627 · Cited by 49 · see citing works

Virtual Staining for Mitosis Detection in Breast Histopathology

Mercan, C., Reijnen-Mooij, G., Tellez, D., Lotz, J., Weiss, N., van Gerven, M. A. J., & Ciompi, F. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098409 · Cited by 48 · see citing works

Self-supervised learning for medical image classification: a systematic review and implementation guidelines

Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01049-7 · Cited by 250 · see citing works

Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images

Lu, M. Y., Chen, B., Zhang, A., Williamson, D. F. K., Chen, R. J., Ding, T., Le, L. P., Chuang, Y.-S., & Mahmood, F. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01893 · Cited by 122 · see citing works

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

Padmanabhan, M., Yuan, P., Chada, G., & Nguyen, H. V. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8071050 · Cited by 75 · see citing works

Breakdown of Machine Learning Algorithms

Meena, M., & Sehrawat, R. (2022) · Recent Trends in Artificial Intelligence & it s Applications · doi:10.46610/rtaia.2022.v01i03.005 · Cited by 1 · see citing works

Interpretable Machine Learning Assessment

Han, H., Wu, Y., Wang, J., & Han, A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2023.126891 · Cited by 29 · see citing works

Application of Machine Learning in Animal Disease Analysis and Prediction

Zhang, S., Su, Q., & Chen, Q. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200728195613 · Cited by 29 · see citing works

Machine Learning Techniques for the Management of Diseases: A Paper Review

Tim, N. K., Kamla, V. C., & Tagne, E. F. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-56396-6_23