The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts.
However, model training is often difficult due to label scarcity in the medical domain, and a model’s usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities.
Wang, Z., Wu, Z., Agarwal, D. & Sun, J. MedCLIP: contrastive learning from unpaired medical images and text. In 3876–3887 .Maleki, D. & Tizhoosh, H. R. LILE: look in-depth before looking elsewhere—a dual attention network using transformers for cross-modal information retrieval in histopathology archives. In 879–894 .
Banerjee, S. & Lavie, A. METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In We thank Jinghao Zhou for providing insights into the training dynamics for iBOT. We thank A. Song for his feedback. This work was supported in part by the BWH president’s fund, BWH and MGH Pathology, and NIH NIGMS R35GM138216 . M.Y.L. was also supported by the Siebel Scholars program. D.F.K.W. was also funded by the NIH NCI Ruth L. Kirschstein National Service Award, T32CA251062. R.J.C. was also supported by the NSF Graduate Fellowship. T.D. was also supported by the Harvard SEAS Fellowship. G.
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