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

2026-09-01

AI Summary

本文使用公开的全切片图像基础模型自动筛选含有肿瘤的切片，通过零样本分类预测排名有效识别关键信息。

Abstract

Patient exams in the cancer diagnosis and staging process typically generate several whole slide images (WSIs). One of the initial steps in training models on WSI data is identifying one or a few slides containing tumor or other diagnostic biomarkers necessary for downstream prediction tasks such as estimating recurrence risk or progression-free survival. This step requires tedious manual curation by experienced pathologists. Many published datasets make the artificial assumption of 1 slide per patient. Alternatively, all slides per patient may be used for model training, which may dilute the signal from the few slides containing tumor or other relevant information. In this paper, we present a pipeline to overcome these challenges using publicly available WSI foundation models (FMs). Our evaluations show that ranking WSIs based on predictions from zero-shot classification using WSI FMs accurately identifies slides with the most tumor, indicating that WSI FMs contain sufficient morphology signal to automatically triage slides. We also present a formulation for ranked evaluation to benchmark FM performance in slide triage. We show, on multiple datasets, that tumor slides are identified in the top-2 ranked slides for patients with up to 43 slides.

Problem

• whole slide images
• tumor identification
• manual curation
• foundation models
• cancer diagnosis

Innovation

• Whole Slide Image Foundation Models
• Automatic Triage
• Zero-shot Classification
• Ranked Evaluation