用自监督方法适配病理大模型，提升膀胱癌手术切片诊断准确率。
DA-SSL: self-supervised domain adaptor to leverage foundational models in turbt histopathology slides
- 设计轻量级自监督适配器，无需微调大模型即可对齐特定切片域特征。
- 在多中心数据上实现0.77±0.04的AUC，外部测试准确率84%、特异性91%。
- 适合临床难解的膀胱癌新辅助化疗响应预测，可直接部署于现有模型管线。
近期基于多实例学习（MIL）与病理基础模型（PFMs）的深度学习框架在组织病理学中表现优异，但因领域偏移问题，在某些癌种或样本类型上受限——如经尿道膀胱肿瘤切除术（TURBT）切片包含碎片化组织和电灼伤伪影，未在公开的PFMs中广泛出现。为此，我们提出一种简单有效的自监督领域适配器（DA-SSL），在不微调预训练模型的前提下，将PFM特征重新对齐至TURBT领域。我们在多中心研究中验证该框架用于预测TURBT患者治疗反应，当前形态学特征尚未充分利用，且识别新辅助化疗受益者极具挑战。结果显示，五折交叉验证下AUC为0.77±0.04，外部测试准确率达0.84，敏感性0.71，特异性0.91（采用多数投票）。结果表明，轻量级自监督领域适应可有效增强基于PFM的MIL流程，适用于临床棘手的病理任务。代码已开源：https://github.com/zhanghaoyue/DA_SSL_TURBT。
原文摘要 · Abstract (English)
Recent deep learning frameworks in histopathology, particularly multiple instance learning (MIL) combined with pathology foundational models (PFMs), have shown strong performance. However, PFMs exhibit limitations on certain cancer or specimen types due to domain shifts - these cancer types were rarely used for pretraining or specimens contain tissue-based artifacts rarely seen within the pretraining population. Such is the case for transurethral resection of bladder tumor (TURBT), which are essential for diagnosing muscle-invasive bladder cancer (MIBC), but contain fragmented tissue chips and electrocautery artifacts and were not widely used in publicly available PFMs. To address this, we propose a simple yet effective domain-adaptive self-supervised adaptor (DA-SSL) that realigns pretrained PFM features to the TURBT domain without fine-tuning the foundational model itself. We pilot this framework for predicting treatment response in TURBT, where histomorphological features are currently underutilized and identifying patients who will benefit from neoadjuvant chemotherapy (NAC) is challenging. In our multi-center study, DA-SSL achieved an AUC of 0.77+/-0.04 in five-fold cross-validation and an external test accuracy of 0.84, sensitivity of 0.71, and specificity of 0.91 using majority voting. Our results demonstrate that lightweight domain adaptation with self-supervision can effectively enhance PFM-based MIL pipelines for clinically challenging histopathology tasks. Code is Available at https://github.com/zhanghaoyue/DA_SSL_TURBT.
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