A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging
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Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002730 · Cited by 1,082 · see citing works
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., & Re, C. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning · doi:10.1145/3368555.3384468 · Cited by 411 · see citing works
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MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., & Ni, B. (2023) · Scientific Data · doi:10.1038/s41597-022-01721-8 · Cited by 1,043 · see citing works
“As a large-scale benchmark in biomedical image analysis, MedMNIST has been particularly useful for machine learning and computer vision research 13–15 , e.g. , AutoML, trustworthy machine learning, domain adaptive learning.”
MethodWISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
Wang, P., Li, Z., Zhang, N., Xu, Z., Yao, Y., Jiang, Y., Xie, P., Huang, F., & Chen, H. (2024) · arXiv · doi:10.48550/arxiv.2405.14768 · Cited by 19 · see citing works
- Authors
- Henn, T., Sakamoto, Y., Jacquet, C., Yoshizawa, S., Andou, M., Tchen, S., Saga, R., Ishihara, H., Shimizu, K., Li, Y., & Tanno, R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 509-518
- ISSN
- 0302-9743
- 1611-3349
- Cited by
- 2
- References
- 2
- Language
- en
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