Histopathology Image Report Generation by Vision Language Model with Multimodal In-Context Learning

• URL: http://arxiv.org/abs/2506.17645v1
• Date: Sat, 21 Jun 2025 08:56:45 GMT
• Title: Histopathology Image Report Generation by Vision Language Model with Multimodal In-Context Learning
• Authors: Shih-Wen Liu, Hsuan-Yu Fan, Wei-Ta Chu, Fu-En Yang, Yu-Chiang Frank Wang,
• Abstract summary: We propose an in-context learning framework called PathGenIC that integrates context derived from the training set with a multimodal in-context learning mechanism.<n>Our method dynamically retrieves semantically similar whole slide representations (WSI)-report pairs and incorporates adaptive feedback to enhance contextual relevance and generation quality.
• Score: 27.49826980862286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automating medical report generation from histopathology images is a critical challenge requiring effective visual representations and domain-specific knowledge. Inspired by the common practices of human experts, we propose an in-context learning framework called PathGenIC that integrates context derived from the training set with a multimodal in-context learning (ICL) mechanism. Our method dynamically retrieves semantically similar whole slide image (WSI)-report pairs and incorporates adaptive feedback to enhance contextual relevance and generation quality. Evaluated on the HistGen benchmark, the framework achieves state-of-the-art results, with significant improvements across BLEU, METEOR, and ROUGE-L metrics, and demonstrates robustness across diverse report lengths and disease categories. By maximizing training data utility and bridging vision and language with ICL, our work offers a solution for AI-driven histopathology reporting, setting a strong foundation for future advancements in multimodal clinical applications.

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