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.

翻译：组织病理学亚型的研究对于制定卵巢癌个体化有效治疗策略具有重要价值。然而，日益增长的诊断工作量对英国病理科提出了挑战，推动了人工智能方法的应用。尽管该领域的传统方法依赖于预计算的冻结图像特征，但近期进展已转向端到端特征提取，虽提升了准确性，却以显著降低训练可扩展性和增加实验耗时作为代价。本文提出一种利用对比学习与原型学习、通过特征空间增强结合预计算冻结特征的新方法，用于卵巢癌组织病理学图像的亚型分类与定位。与DSMIL相比，我们的方法在实例级和玻片级分类的F1分数上分别提升了70.4%和15.3%，同时在实例定位的AUC上提升了16.9%，在玻片分类的AUC上提升了2.3%，且保持了冻结切片特征的使用。