通过薛定谔桥：法医死后自溶前图像修复的基准测试以增强法医诊断

中文摘要 AI 辅助

法医组织病理学是确定死因和疾病诊断的关键，但受到死后自溶的严重阻碍，死后自溶是一种不可逆的随机降解过程，会扭曲组织形态并引入诊断主观性，因此将自溶图像恢复到诊断上合理的自溶前状态对于提高法医实践的客观性具有重要价值。该修复任务极具挑战性，因为自溶会导致大量非确定性形态变化，且无法获得像素级配对数据，这使得监督学习及循环/结构一致的无配对翻译方法的假设失效。为解决此问题，我们将法医组织病理学自溶修复定义为一项新任务：在无配对监督下，将严重自溶的死后图像转换为具有诊断意义的“自溶前”表征。我们贡献了AutoPath，这是该问题的首个同源无配对数据集，通过将标本分割为相邻组织块构建而成——其中一块立即处理，另一块暴露以诱导自溶，共产生来自69例不同肝脏病症病例的近10000个10×图像块。我们进一步将该问题建模为自溶与非自溶分布之间的薛定谔桥，为随机、严重的形态降解提供了一种原则性建模方法。关键是，我们证明了通用图像级生成指标（如FID）与诊断效用不匹配，并提出了基于法医的、切片级诊断分布一致性评估方法。总体而言，本研究建立了一个可复现的基准（涵盖任务定义、真实世界数据集和评估方法），以推动法医病理学自溶修复领域严谨且具有实际意义的进展。

英文摘要

Forensic histopathology, essential for determining cause of death and disease diagnosis, is severely impeded by postmortem autolysis, i.e., an irreversible, stochastic degradation process that distorts tissue morphology and introduces diagnostic subjectivity, thereby underscoring the value of restoring autolyzed images to a diagnostically plausible, pre-autolysis state for improving objectivity in forensic practice. This restoration task is fundamentally challenging due to the large, non-deterministic morphological changes caused by autolysis and the infeasibility of pixel-wise paired data, which invalidates assumptions underlying supervised and cycle/structure-consistent unpaired translation methods. To address this, we formalize forensic histopathology autolysis restoration as a new task: under unpaired supervision, transform postmortem images with severe autolysis into diagnostically meaningful ``antemortem'' representations. We contribute AutoPath, the first homologous yet unpaired dataset for this problem, constructed by splitting specimens into adjacent tissue blocks---one processed immediately, the other exposed to induce autolysis---yielding nearly ten thousand $10\times$ patches from 69 cases with varying liver conditions. We further frame the problem as a Schrödinger Bridge between the autolyzed and non-autolyzed distributions, offering a principled approach to modeling stochastic, severe morphological degradation. Critically, we demonstrate the misalignment of generic image-level generative metrics (e.g., FID) with diagnostic utility and propose a forensically grounded, slide-level diagnostic distribution consistency evaluation. Overall, this work establishes a reproducible benchmark (encompassing task definition, a real-world dataset, and an evaluation methodology) toward rigorous and practically meaningful progress in autolysis restoration for forensic pathology.

发表机构

• Xi’an Jiaotong University（西安交通大学）
• Sun Yat-Sen University（中山大学）

机构由 AI 辅助整理，请以论文原文为准。