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.
@article{arxiv.2506.17645,
title = {Histopathology Image Report Generation by Vision Language Model with Multimodal In-Context Learning},
author = {Shih-Wen Liu and Hsuan-Yu Fan and Wei-Ta Chu and Fu-En Yang and Yu-Chiang Frank Wang},
journal= {arXiv preprint arXiv:2506.17645},
year = {2025}
}