发表机构
University of Houston; Houston Methodist Academic Institute; Houston Methodist Hospital System（休斯顿大学; 休斯顿卫理公会学术研究所; 休斯顿卫理公会医院系统）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对慢性伤口管理中的分类、分割与定位三任务耦合问题，提出统一框架WILLIE，整合三个公共数据集并对比统一模型与10个单任务基线，最佳模型单次前向实现91.88%准确率、91.41% Dice和96.23% AP@0.5，证明分割定位优于检测定位。
AI 中文摘要
慢性伤口管理影响美国超过820万患者，并带来沉重的临床和经济负担。临床伤口评估通常涉及三个相互关联的任务：识别伤口类型、勾画伤口边界以及定位伤口区域以进行测量和监测。尽管存在这种临床耦合性，现有的机器学习方法通常使用单独的模型来处理伤口分类、分割和定位。我们提出了WILLIE，一个用于伤口分类、分割和定位的统一框架与基准，能够在通用协议下对多任务伤口分析进行系统性评估。WILLIE将三个公共伤口数据集整合为一个共享基准，并在三种缩放配置下将统一模型与10个单任务基线进行比较。最佳模型在单次前向传播中同时产生所有三种输出，实现了91.88%的分类准确率、91.41%的Dice系数和96.23%的AP@0.5。除了总体性能外，我们的结果表明，在该基准中，基于分割的定位优于专门的检测基线，这表明对于空间连贯的伤口目标，基于框的定位可能是不必要的。我们的研究结果强调，医疗影像中有效的多任务学习不仅依赖于共享表示，还依赖于任务制定、兼容性和基准设计。
英文摘要
Chronic wound management affects over 8.2 million patients in the United States and imposes substantial clinical and economic burden. Clinical wound assessment commonly involves three coupled tasks: identifying wound type, delineating wound boundaries, and localizing the wound region for measurement and monitoring. Despite this clinical coupling, existing machine learning approaches typically address wound classification, segmentation, and localization using separate models. We present WILLIE, a unified framework and benchmark for wound classification, segmentation and localization that enables systematic evaluation of multi-task wound analysis under a common protocol. WILLIE harmonizes three public wound datasets into a shared benchmark and compares unified models across three scaling configurations against 10 single-task baselines. The best model achieves 91.88% classification accuracy, 91.41% Dice, and 96.23% AP@0.5 while producing all three outputs in a single forward pass. Beyond aggregate performance, our results show that segmentation-derived localization outperforms dedicated detection baselines in this benchmark, suggesting that box-based localization may be unnecessary for spatially coherent wound targets. Our findings highlight that effective multi-task learning in healthcare imaging depends not only on shared representations, but also on task formulation, compatibility, and benchmark design.
Comments21 pages, 4 figures, 9 tables. Published in Proceedings of the 11th Machine Learning for Healthcare Conference (MLHC 2026), PMLR 340:1243-1263. Code: https://github.com/Qian-Group-HRI/Willie
Journal refProceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:1243-1263, 2026
