A position-aware sets based weakly supervised framework for whole-slide subtype classification

Jiuman Song, Bo Yu, Xiaomin Liu, Lele Cong, Zilong Zhou, Xianling Cong, Hongyan Sun, Shuchao Pang, Hechang Chen

Abstract: Identifying cancer subtypes is essential for personalized treatment and accurate prognosis due to the varying sensitivities of subtypes to therapies. However, in cancer subtype classification tasks, normal slides are usually scarce or absent as negative samples, while subtype whole-slide images (WSIs) often contain extensive unannotated normal tissue regions. These regions introduce significant noise during feature fusion and subtype classification, leading to degraded performance of existing weakly supervised methods In this paper, we propose the Position-aware Sets based Weakly Supervised learning framework (PSWS), designed for cancer subtype classification using WSIs, with a two-stage structure to enhance model efficiency. Specifically, it first presents a novel patch organization approach, distinct from the bag concept of traditional Multiple Instance Learning (MIL), called position-aware sets, as basic units for learning. Then, PSWS automatically selects subtype-specific features based on enhanced histological features and mutual-patch relations, mitigating the negative impact of unannotated negative regions. In the experiments, the superior performance of PSWS over representative MILs is validated through subtype classification tasks on both public datasets and our internally constructed dataset. Furthermore, class probabilities of position-aware sets and attention region visualizations demonstrate its post-hoc interpretability, assisting pathologists in locating suspicious areas.

External IDs:dblp:journals/eaai/SongYLCZCSPC25