Hybrid multiple instance learning network for weakly supervised medical image classification and localization

Qi Lai, Chi-Man Vong, Tao Yan, Pak-Kin Wong, Xiaokun Liang

Abstract: Highlights•A novel hybrid MIL network is proposed that combines CNNs with the BLS to capture multiple-level feature information, and global-level semantics information, and estimates the inter-correlation between them, forming a single framework.•The proposed HybridMIL not only includes the cognitive process of visual appearance but also the enhanced representation process of instance and semantic level correlations.•Without any other mechanism, the proposed HybridMIL framework can easily and effectively achieve comparable classification and localization performance on public medical datasets.