[Submitted on 6 Nov 2026 (v1), last revised 10 Nov 2026 (this version, v3)]
Abstract:Pathology reporting translates tissue observations across regions and slides into diagnoses and supporting morphological descriptions, yet explicit tissue-composition measurements are rarely used to guide H&E-based report generation. We developed PathXAI, which pairs transferable tissue segmentation with a pretrained vision-language model. Because supplying tissue information directly with H&E images gave limited benefit, PathXAI-Report uses segmentation overlays and tissue statistics in separate steps, selects between candidate diagnoses by fixed rules, acquires further images when evidence is insufficient and records every step for review, without updating model weights. Its segmentation component exceeded baselines in mean Dice on internal and external tests. In automated evaluation on 1,200 public and 151 private cases, PathXAI achieved higher agreement with reference diagnoses and documented morphology than H&E-based comparators. On the public cohort, it exceeded Qwen+H&E by 4.4 percentage points in cancer family match rate and 5.7 percentage points in finding F1.
Submission history
From: Zhaorui Tan [view email]
[v1] Fri, 6 Nov 2026 07:54:59 UTC (27,841 KB)
[v2] Mon, 9 Nov 2026 04:34:49 UTC (24,737 KB)
[v3] Tue, 10 Nov 2026 11:57:05 UTC (33,481 KB)
