AI 中文总结
针对现有概念模型跨站点泛化差、适配FVLMs成本高的问题，提出开放语言概念统一学习框架UniCon，通过共享语义空间等三项创新实现多模态可解释皮肤病诊断，获顶级准确率且具备跨站点干预能力。
AI 中文摘要
人类可解释的计算机辅助诊断对临床决策至关重要。基于概念的模型因能提供透明推理并支持事后临床医生参与干预而表现出色，但其严格的特定数据集适配性固有地限制了跨站点泛化。由于不同队列中概念分类法在可用性、粒度和语义上存在异质性，将其应用于皮肤镜图像和临床照片等多种模态极具挑战性。因此，适配基础视觉语言模型（FVLMs）需要高昂的标签工程成本和重复的后训练。现有的干预机制仍严格绑定于预定义概念，缺乏适应性，阻碍了可扩展皮肤病计算机辅助诊断（CAD）的部署。为解决这些瓶颈，我们提出UniCon，一种用于多模态可解释视觉语言诊断的开放语言统一概念学习框架。UniCon通过三项贡献应对这些挑战：（1）通过统一概念原型码本构建共享语义表示空间，无需特定数据集的重新训练即可无缝协调跨模态的异质概念系统；（2）基于开放语言的多方面语义规范，克服文本标签稀疏的限制，提升不确定临床情境下的边界敏感性；（3）由可靠性门控瓶颈聚合驱动的稳健跨站点可调干预接口，实现一致推理和可转移的临床医生修正。大量实验表明，UniCon在确保顶级诊断准确率之外，还成功弥合了不同的临床分类法，释放了前所未有的跨站点干预能力。代码可在该https URL获取。
英文摘要
Human-interpretable computer-aided diagnosis is crucial for clinical decision making. Concept-based models excel by providing transparent reasoning and enabling post-hoc, clinician-in-the-loop interventions. However, their rigid dataset-specific adaptation inherently restricts cross-site generalization. Applying them across diverse modalities, such as dermoscopic and clinical photographs, is challenging due to heterogeneous concept taxonomies varying in availability, granularity, and semantics across cohorts. Consequently, adapting Foundation Vision-Language Models (FVLMs) demands costly label engineering and repeated post-training. Existing intervention mechanisms remain rigidly tied to predefined concepts, lacking adaptability and hindering scalable dermatology CAD deployment. To address these bottlenecks, we propose UniCon, an open-linguistic unified concept learning framework for multimodal interpretable vision-language diagnosis. UniCon resolves these challenges through three contributions: (1) A shared semantic representation space via a unified concept prototype codebook, seamlessly coordinating heterogeneous concept systems across modalities without dataset-specific retraining. (2) Open-linguistic based multi-faceted semantic specifications to overcome sparse textual label limitations, improving boundary sensitivity in uncertain clinical contexts. (3) A robust, cross-site adjustable intervention interface powered by reliability-gated bottleneck aggregation, enabling consistent reasoning and transferable clinician corrections. Extensive experiments demonstrate that beyond securing top-tier diagnostic accuracy, UniCon successfully bridges disparate clinical taxonomies, unlocking unprecedented cross-site intervention capabilities. Code is available at https://github.com/wuchengyu123/UniCon.
Commentsaccepted by ACM Multimedia 2026
