MB-DSMIL-CL-PL: 可扩展的弱监督卵巢癌亚型分类与定位使用对比学习和原型学习与冻结的图像块特征
MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features
AI总结：
本文提出了一种基于对比学习和原型学习的弱监督方法，利用预计算的冻结特征进行卵巢癌亚型分类和定位，显著提升了分类和定位的性能指标。
摘要：
对组织病理学亚型的研究对于制定有效的卵巢癌治疗策略具有重要价值。然而，增加的诊断工作量给英国病理部门带来了挑战，导致AI方法的兴起。虽然该领域的传统方法依赖于预计算的冻结图像特征，但最近的进展转向了端到端的特征提取，虽然提高了准确性，但牺牲了训练过程中的显著可扩展性和耗时的实验。在本文中，我们提出了一种新的方法，用于使用对比学习和原型学习以及预计算的冻结特征进行卵巢癌组织病理学图像的亚型分类和定位，通过特征空间增强。与DSMIL相比，我们的方法在实例级和切片级分类的F1分数上分别提高了70.4%和15.3%，同时在实例定位和切片分类上的AUC分别提高了16.9%和2.3%，同时保持了冻结图像块特征的使用。
英文摘要：
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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