用少量病理切片即可实现脑肿瘤精准分类，无需复杂调优。
Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification
- 仅需每例10张切片，预训练模型即可达到高精度分类。
- 简单微调（如线性探测）效果优于全量微调，后者反而降低性能。
- 适合临床病理医生快速部署AI辅助诊断系统。
基于大规模病理数据预训练的基底模型在多种诊断任务中表现优异。本文系统评估了其在脑肿瘤分类中的迁移学习策略，涵盖254例五类主要肿瘤：胶质母细胞瘤、星形细胞瘤、少突胶质细胞瘤、原发性中枢神经系统淋巴瘤和转移性肿瘤。对比先进基底模型与传统方法发现，即使每例仅使用10张切片，基底模型仍能实现稳健分类性能，突破了以往需大量图像采样的假设。此外，评估显示简单迁移策略（如线性探测）已足够有效，而全量微调常导致性能下降。研究提示应从‘在大量病理数据上训练编码器’转向‘用标注数据查询预训练编码器’，为临床病理中AI辅助诊断的实际落地提供重要启示。
原文摘要 · Abstract (English)
Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a systematic evaluation of transfer learning strategies for brain tumor classification using these models. We analyzed 254 cases comprising five major tumor types: glioblastoma, astrocytoma, oligodendroglioma, primary central nervous system lymphoma, and metastatic tumors. Comparing state-of-the-art foundation models with conventional approaches, we found that foundation models demonstrated robust classification performance with as few as 10 patches per case, despite the traditional assumption that extensive per-case image sampling is necessary. Furthermore, our evaluation revealed that simple transfer learning strategies like linear probing were sufficient, while fine-tuning often degraded model performance. These findings suggest a paradigm shift from "training encoders on extensive pathological data" to "querying pre-trained encoders with labeled datasets", providing practical implications for implementing AI-assisted diagnosis in clinical pathology.
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