arXiv cs.CL· Zhaorui Tan, Jun Aun Tan, Weixiang Zhong, Xiaoxi Wang, Tan Pan, Xingdong Xue, Li Liu, Jing Zhang, Jing Zhang, Weimiao Yu·· 11 小时前AI 评分31
PathXAI：用可迁移组织分割引导结构化病理报告生成
PathXAI: Evidence-guided structured pathology reporting by transferable tissue segmentation
AI 导读
PathXAI 将分割得到的组织信息与 H&E 图像及可选领域知识结合，引导诊断推理、针对性复检和结构化报告撰写，其分割组件在内部与外部测试中平均 Dice 均超过基线。
正文 · 原文
Abstract:Pathology reporting requires translating tissue observations across regions and slides into specific diagnoses and supporting morphological descriptions. However, the use of explicit tissue-composition measurements to guide H&E-based report generation remains underexplored. Thus, we developed PathXAI, which combines segmentation-derived tissue information with H&E images and optional domain knowledge to guide diagnostic reasoning, targeted reinspection and structured report writing. Its segmentation component exceeded baselines in mean Dice on internal and external tests. In automated report evaluation on 1,200 public and 151 private cases, PathXAI achieved higher agreement with reference diagnoses and documented morphology than the H&E-based comparators. On the public evaluation cohort, PathXAI-Report exceeded Qwen + H&E in cancer family match rate by 4.4 percentage points and in finding F1 by 5.7 percentage points. PathXAI generates evidence-linked reports with a self-hosted pretrained vision-language model, without updating its weights.
| Comments: | More experimental results will be added in the future version |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.09117 [cs.CV] |
| (or arXiv:2611.09117v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.09117 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhaorui Tan [view email]
[v1]
Fri, 6 Nov 2026 07:54:59 UTC (27,841 KB)
来源：arXiv cs.CL · arxiv.org