IIB-MIL: Integrated Instance-Level and Bag-Level Multiple Instances Learning with Label Disambiguation for Pathological Image Analysis

Qin Ren, Yu Zhao, Bing He, Bingzhe Wu, Sijie Mai, Fan Xu, Yueshan Huang, Yonghong He, Junzhou Huang, Jianhua Yao

Abstract: Digital pathology plays a pivotal role in the diagnosis and interpretation of diseases and has drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and diverse nature of whole-slide images (WSIs), analyzing them through multiple instance learning (MIL) has become a widely-used scheme, which, however, faces the challenges that come with the weakly supervised nature of MIL. Conventional MIL methods mostly either utilized instance-level or bag-level supervision to learn informative representations from WSIs for downstream tasks. In this work, we propose a novel MIL method for pathological image analysis with integrated instance-level and bag-level supervision (termed IIB-MIL). More importantly, to overcome the weakly supervised nature of MIL, we design a label-disambiguation-based instance-level supervision for MIL using Prototypes and Confidence Bank to reduce the impact of noisy labels. Extensive experiments demonstrate that IIB-MIL outperforms state-of-the-art approaches in both benchmarking datasets and addressing the challenging practical clinical task. The code is available at https://github.com/TencentAILabHealthcare/IIB-MIL.