Generating radiology reports from 3D volumetric data remains challenging due to the difficulty of grounding fine-grained pathologies within high-dimensional scans.While retrievalaugmented generation (RAG) offers a potential solution, standard approaches struggle with visual-semantic ambiguity and often introduce irrelevant "normal" context that dilutes pathological signals.To address this limitation, we introduce CPR-RAG, a model-agnostic RAG framework that enhances organ-level grounding by integrating clinical priors into the retrieval process.Specifically, we propose a clinical prior-regularized re-ranking module that leverages corpus-derived co-occurrence statistics to align retrieved candidates with latent disease distributions, ensuring clinical consistency beyond mere visual similarity.Furthermore, we employ clinical relevance context refinement to selectively filter out boilerplate normal descriptions, thereby maximizing the information density of the evidence provided to the generator.Extensive experiments on the RadGenome-ChestCT benchmark demonstrate that CPR-RAG significantly improves clinical efficacy across state-of-theart radiology report generation models.Human evaluation further confirms that our approach achieves superior factual correctness, completeness, and utility compared to the existing models.

Accurate tumor segmentation in whole slide image (WSI) is essential for histopathological diagnosis and research, but the traditional manual analysis is labor-intensive and prone to variability. Furthermore, many artificial models focus on specific magnification images, limiting the detailed information available for segmentation. To address these challenges, we propose MFTrans, a novel multi-resolution fusion transformer with a CNN-based architecture designed for efficient tumor segmentation in WSI. Inspired by the diagnostic procedures of expert pathologists, MFTrans integrates both high- and low-magnification images, capturing detailed local features and broader contextual relationships through a dual-branch architecture. The model employs a global token transformer and cross-attention mechanism to fuse hierarchical features from dual branches to improve segmentation performance. We evaluate MFTrans on three real-world WSI datasets: Camelyon16, PAIP2019, and Catholic Uijeongbu St. Mary's hospital dataset, demonstrating its superior segmentation performance over state-of-the-art methods in balanced and imbalanced setups. These results highlight MFTrans's effectiveness in medical image analysis and its generalizability across different datasets, making it a robust tool for automated cancer diagnostics. Our code is available at https://github.com/aimed-gist/MFTrans.