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arXiv Machine Learning By Ayushi Sinha, Shashank Yadav, Benjamin Holmes, Pravat Das, Aaron W. Bogan, James S. Lewis Jr., Santiago Romero-Brufau, Andrew Y. K. Foong, Scott H. Kaufmann, Kathryn M. Van Abel, David M. Routman, Michael R. Lucas

Morphology signal in whole slide image foundation models can automatically triage slides

Read the original on arXiv Machine Learning →

The paper introduces a pipeline that uses publicly available whole slide image foundation models (FMs) to automatically triage slides by ranking them based on zero‑shot classification predictions. This approach accurately identifies slides containing the most tumor, achieving top‑2 ranking for patients with up to 43 slides across multiple datasets. The study also proposes a ranked evaluation framework to benchmark FM performance in slide triage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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