{"ID":2883797,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08069","arxiv_id":"2508.08069","title":"Information Bottleneck-based Causal Attention for Multi-label Medical Image Recognition","abstract":"Multi-label classification (MLC) of medical images aims to identify multiple diseases and holds significant clinical potential. A critical step is to learn class-specific features for accurate diagnosis and improved interpretability effectively. However, current works focus primarily on causal attention to learn class-specific features, yet they struggle to interpret the true cause due to the inadvertent attention to class-irrelevant features. To address this challenge, we propose a new structural causal model (SCM) that treats class-specific attention as a mixture of causal, spurious, and noisy factors, and a novel Information Bottleneck-based Causal Attention (IBCA) that is capable of learning the discriminative class-specific attention for MLC of medical images. Specifically, we propose learning Gaussian mixture multi-label spatial attention to filter out class-irrelevant information and capture each class-specific attention pattern. Then a contrastive enhancement-based causal intervention is proposed to gradually mitigate the spurious attention and reduce noise information by aligning multi-head attention with the Gaussian mixture multi-label spatial. Quantitative and ablation results on Endo and MuReD show that IBCA outperforms all methods. Compared to the second-best results for each metric, IBCA achieves improvements of 6.35\\% in CR, 7.72\\% in OR, and 5.02\\% in mAP for MuReD, 1.47\\% in CR, and 1.65\\% in CF1, and 1.42\\% in mAP for Endo.","short_abstract":"Multi-label classification (MLC) of medical images aims to identify multiple diseases and holds significant clinical potential. A critical step is to learn class-specific features for accurate diagnosis and improved interpretability effectively. However, current works focus primarily on causal attention to learn class-...","url_abs":"https://arxiv.org/abs/2508.08069","url_pdf":"https://arxiv.org/pdf/2508.08069v1","authors":"[\"Xiaoxiao Cui\",\"Yiran Li\",\"Kai He\",\"Shanzhi Jiang\",\"Mengli Xue\",\"Wentao Li\",\"Junhong Leng\",\"Zhi Liu\",\"Lizhen Cui\",\"Shuo Li\"]","published":"2025-08-11T15:12:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}