MPath：从全切片图像生成多模态病历报告
MPath: Multimodal Pathology Report Generation from Whole Slide Images
AI总结：
MPath通过多模态条件化方法，利用预训练的生物医学语言模型生成病理报告，展示了在病理报告生成中的有效性与可扩展性。
摘要：
自动从全切片图像（WSIs）生成诊断病理报告是计算病理学中一个新兴方向。将高分辨率组织模式转化为临床一致的文本仍然困难，由于形态学差异大和病理叙述的复杂结构。我们介绍了MPath，一个轻量级的多模态框架，通过学习的视觉前缀提示机制，将WSI衍生的视觉嵌入条件地输入到预训练的生物医学语言模型（BioBART）中。而不是端到端的视觉-语言预训练，MPath利用基础模型WSI特征（CONCH + Titan）并通过紧凑的投影模块将其注入到BioBART中，保持语言骨干冻结以提高稳定性和数据效率。MPath是在RED 2025大奖挑战数据集上开发和评估的，并在测试阶段2中排名第四，尽管提交机会有限。结果突显了基于提示的多模态条件化作为病理报告生成可扩展且可解释的策略的潜力。
英文摘要：
Automated generation of diagnostic pathology reports directly from whole slide images (WSIs) is an emerging direction in computational pathology. Translating high-resolution tissue patterns into clinically coherent text remains difficult due to large morphological variability and the complex structure of pathology narratives. We introduce MPath, a lightweight multimodal framework that conditions a pretrained biomedical language model (BioBART) on WSI-derived visual embeddings through a learned visual-prefix prompting mechanism. Instead of end-to-end vision-language pretraining, MPath leverages foundation-model WSI features (CONCH + Titan) and injects them into BioBART via a compact projection module, keeping the language backbone frozen for stability and data efficiency. MPath was developed and evaluated on the RED 2025 Grand Challenge dataset and ranked 4th in Test Phase 2, despite limited submission opportunities. The results highlight the potential of prompt-based multimodal conditioning as a scalable and interpretable strategy for pathology report generation.
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