2025/11/04 by Song, Yucheng, Ge, Yifan, Li, Junhao +2
Computer Science · #Component (thermodynamics) #Domain (mathematical analysis) #Domain Adaptation and Few-Shot Learning #Domain knowledge #Embedding #Intervention (counseling) #Key (lock) #Multimodal Machine Learning Applications #Spurious relationship #Task (project management) #Task analysis #Topic Modeling
paper · open access · doi:10.48550/arxiv.2511.02271
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/11/04 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28
Medical Report Generation (MRG) is a key part of modern medical diagnostics, as it automatically generates reports from radiological images to reduce radiologists' burden. However, reliable MRG models for lesion description face three main challenges: insufficient domain knowledge understanding, poor text-visual entity embedding alignment, and spurious correlations from cross-modal biases. Previous work only addresses single challenges, while this paper tackles all three via a novel hierarchical task decomposition approach, proposing the HTSC-CIF framework. HTSC-CIF classifies the three challenges into low-, mid-, and high-level tasks: 1) Low-level: align medical entity features with spatial locations to enhance domain knowledge for visual encoders; 2) Mid-level: use Prefix Language Modeling (text) and Masked Image Modeling (images) to boost cross-modal alignment via mutual guidance; 3) High-level: a cross-modal causal intervention module (via front-door intervention) to reduce confounders and improve interpretability. Extensive experiments confirm HTSC-CIF's effectiveness, significantly outperforming state-of-the-art (SOTA) MRG methods. Code will be made public upon paper acceptance.