Historical Report Guided Bi-modal Concurrent Learning for Pathology Report Generation

• URL: http://arxiv.org/abs/2506.18658v1
• Date: Mon, 23 Jun 2025 14:00:21 GMT
• Title: Historical Report Guided Bi-modal Concurrent Learning for Pathology Report Generation
• Authors: Ling Zhang, Boxiang Yun, Qingli Li, Yan Wang,
• Abstract summary: Historical Report Guided textbfBi-modal Concurrent Learning Framework for Pathology Report textbfGeneration (BiGen) emulating pathologists' diagnostic reasoning.<n>BiGen retrieves WSI-relevant knowledge from pre-built medical knowledge bank by matching high-attention patches.<n>Our framework achieves state-of-the-art performance with 7.4% relative improvement in NLP metrics and 19.1% enhancement in classification metrics for Her-2 prediction versus existing methods.
• Score: 14.8602760818616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated pathology report generation from Whole Slide Images (WSIs) faces two key challenges: (1) lack of semantic content in visual features and (2) inherent information redundancy in WSIs. To address these issues, we propose a novel Historical Report Guided \textbf{Bi}-modal Concurrent Learning Framework for Pathology Report \textbf{Gen}eration (BiGen) emulating pathologists' diagnostic reasoning, consisting of: (1) A knowledge retrieval mechanism to provide rich semantic content, which retrieves WSI-relevant knowledge from pre-built medical knowledge bank by matching high-attention patches and (2) A bi-modal concurrent learning strategy instantiated via a learnable visual token and a learnable textual token to dynamically extract key visual features and retrieved knowledge, where weight-shared layers enable cross-modal alignment between visual features and knowledge features. Our multi-modal decoder integrates both modals for comprehensive diagnostic reports generation. Experiments on the PathText (BRCA) dataset demonstrate our framework's superiority, achieving state-of-the-art performance with 7.4\% relative improvement in NLP metrics and 19.1\% enhancement in classification metrics for Her-2 prediction versus existing methods. Ablation studies validate the necessity of our proposed modules, highlighting our method's ability to provide WSI-relevant rich semantic content and suppress information redundancy in WSIs. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/BiGen.

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