This research introduces Information Bottleneck-based Causal Attention (IBCA), a novel framework designed to improve multi-label medical image recognition. The authors address the tendency of standard models to focus on class-irrelevant features or spurious correlations, which can lead to inaccurate diagnoses. By implementing a Gaussian mixture variational information bottleneck, the system filters out background noise and isolates essential class-specific data. Furthermore, a contrastive enhancement-based causal intervention is used to refine these features, ensuring the model identifies the true causal factors of a disease. Tested on datasets like MuReD and Endo, IBCA significantly outperformed existing state-of-the-art methods in accuracy and interpretability. This approach marks the first time information bottleneck theory has been integrated with causality learning for medical image analysis. Source: August 2025 Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognition Shandong University, Shandong First Medical University, Case Western Reserve University Xiaoxiao Cui, Yiran Li, Kai He, Shanzhi Jiang, Mengli Xue, Wentao Li, Junhong Leng, Zhi Liu, Lizhen Cui, Shuo Li https://arxiv.org/pdf/2508.08069