SurgWound-Bench：面向外科伤口诊断的基准
SurgWound-Bench: A Benchmark for Surgical Wound Diagnosis
AI总结：
本文提出首个开源外科伤口数据集SurgWound及诊断基准，并设计三阶段学习框架WoundQwen，通过多模型协同实现伤口特征分析、感染风险评估与报告生成，推动个性化伤口护理。
摘要：
手术部位感染（SSI）是最常见且成本高昂的医疗相关感染之一，外科伤口护理在预防SSI和改善患者预后方面仍然是一项重大的临床挑战。尽管近期研究探索了利用深度学习进行初步外科伤口筛查，但进展受到数据隐私担忧和专家标注高成本的阻碍。目前，尚无公开可用的数据集或基准涵盖各种类型的外科伤口，导致缺乏开源的外科伤口筛查工具。为弥补这一空白：（1）我们提出了SurgWound，这是第一个开源数据集，包含多样化的外科伤口类型。它包含697张外科伤口图像，由3名专业外科医生标注了八个细粒度临床属性。（2）基于SurgWound，我们引入了首个外科伤口诊断基准，包括视觉问答（VQA）和报告生成任务，以全面评估模型性能。（3）此外，我们提出了一个三阶段学习框架WoundQwen，用于外科伤口诊断。在第一阶段，我们采用五个独立的多模态大语言模型（MLLMs）来准确预测特定的外科伤口特征。在第二阶段，这些预测作为额外的知识输入提供给两个负责诊断结果的MLLMs，用于评估感染风险并指导后续干预。在第三阶段，我们训练一个MLLM，整合前两个阶段的诊断结果以生成综合报告。该三阶段框架能够分析详细的外科伤口特征，并根据外科图像为患者提供后续指导，为个性化伤口护理、及时干预和改善患者预后铺平了道路。
英文摘要：
Surgical site infection (SSI) is one of the most common and costly healthcare-associated infections and and surgical wound care remains a significant clinical challenge in preventing SSIs and improving patient outcomes. While recent studies have explored the use of deep learning for preliminary surgical wound screening, progress has been hindered by concerns over data privacy and the high costs associated with expert annotation. Currently, no publicly available dataset or benchmark encompasses various types of surgical wounds, resulting in the absence of an open-source Surgical-Wound screening tool. To address this gap: (1) we present SurgWound, the first open-source dataset featuring a diverse array of surgical wound types. It contains 697 surgical wound images annotated by 3 professional surgeons with eight fine-grained clinical attributes. (2) Based on SurgWound, we introduce the first benchmark for surgical wound diagnosis, which includes visual question answering (VQA) and report generation tasks to comprehensively evaluate model performance. (3) Furthermore, we propose a three-stage learning framework, WoundQwen, for surgical wound diagnosis. In the first stage, we employ five independent MLLMs to accurately predict specific surgical wound characteristics. In the second stage, these predictions serve as additional knowledge inputs to two MLLMs responsible for diagnosing outcomes, which assess infection risk and guide subsequent interventions. In the third stage, we train a MLLM that integrates the diagnostic results from the previous two stages to produce a comprehensive report. This three-stage framework can analyze detailed surgical wound characteristics and provide subsequent instructions to patients based on surgical images, paving the way for personalized wound care, timely intervention, and improved patient outcomes.
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