用冻结特征+对比与原型学习，高效准确识别卵巢癌亚型及定位。
MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features
- 基于预计算冻结特征，结合对比与原型学习提升模型性能。
- 实例级分类F1提升70.4%，切片级分类AUC提高2.3%。
- 兼顾高精度与训练可扩展性，适合病理图像大规模分析。
卵巢癌组织病理亚型研究对个性化治疗至关重要。然而，英国病理科诊断负荷持续增加，推动了人工智能方法的应用。传统方法依赖预计算的冻结图像特征，而近期进展转向端到端特征提取，虽提升精度但显著降低训练可扩展性并延长实验时间。本文提出一种新方法，利用预计算的冻结特征，通过特征空间增强实现对比学习与原型学习，用于卵巢癌病理图像的亚型分类与定位。相比DSMIL，本方法在实例级分类中F1得分提升70.4%，切片级分类提升15.3%；实例定位的AUC提升16.9%，切片分类AUC提升2.3%，同时保持冻结特征的使用。
原文摘要 · Abstract (English)
The study of histopathological subtypes is valuable for the personalisation of effective treatment strategies for ovarian cancer. However, increasing diagnostic workloads present a challenge for UK pathology departments, leading to the rise in AI approaches. While traditional approaches in this field have relied on pre-computed, frozen image features, recent advances have shifted towards end-to-end feature extraction, providing an improvement in accuracy but at the expense of significantly reduced scalability during training and time-consuming experimentation. In this paper, we propose a new approach for subtype classification and localisation in ovarian cancer histopathology images using contrastive and prototype learning with pre-computed, frozen features via feature-space augmentations. Compared to DSMIL, our method achieves an improvement of 70.4\% and 15.3\% in F1 score for instance- and slide-level classification, respectively, along with AUC gains of 16.9\% for instance localisation and 2.3\% for slide classification, while maintaining the use of frozen patch features.
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