利用基础模型自动识别癌病理切片中的关键肿瘤切片，省去人工筛选。
Morphology signal in whole slide image foundation models can automatically triage slides
- 用零样本分类法对全切片图像进行评分，按肿瘤信号强度排序
- 在43张切片的患者中，肿瘤切片有92%出现在前两名排名
- 适合需要高效筛选病理切片的研究者与临床辅助诊断系统
癌症诊断和分期过程中，每位患者通常产生多张全切片图像（WSI）。训练模型时，需先识别出含肿瘤或诊断标志物的关键切片，以支持复发风险或无进展生存期等下游任务。当前方法要么假设每例仅一张切片，要么使用全部切片，后者可能稀释关键信号。本文提出基于公开可得的全切片图像基础模型（FMs）的自动筛选流程。评估表明，通过零样本分类预测对切片排序，能准确识别出肿瘤信号最强的切片，证明基础模型已蕴含足够形态学信息用于自动切片分诊。我们还提出一种排序评估范式来量化模型在切片筛选中的表现。在多个数据集上验证，对于最多含43张切片的患者，肿瘤切片有92%位于前两名。
原文摘要 · Abstract (English)
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.
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