用多尺度视觉融合提升医学图像异常病种识别能力
Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection
- 通过跨尺度视觉融合增强医学图像细节表征
- 在三个公开数据集上显著优于现有方法
- 适合医疗AI误诊防护与罕见病检测场景
在可信医疗诊断系统中，引入分布外（OOD）检测旨在识别样本中的未知疾病，从而降低误诊风险。本研究提出一种基于视觉语言模型（VLMs）的新颖OOD检测框架，通过整合多层次视觉信息应对与已知疾病相似的难辨未知疾病。具体而言，提出跨尺度视觉融合策略，耦合多尺度视觉嵌入，丰富医学图像的细节表征，提升未知疾病的判别能力。此外，设计跨尺度硬伪OOD样本生成策略，最大化提升OOD检测性能。在三个公开医疗数据集上的实验验证表明，所提框架在OOD检测性能上优于现有方法。源代码可在https://openi.pcl.ac.cn/OpenMedIA/HVL获取。
原文摘要 · Abstract (English)
In trustworthy medical diagnosis systems, integrating out-of-distribution (OOD) detection aims to identify unknown diseases in samples, thereby mitigating the risk of misdiagnosis. In this study, we propose a novel OOD detection framework based on vision-language models (VLMs), which integrates hierarchical visual information to cope with challenging unknown diseases that resemble known diseases. Specifically, a cross-scale visual fusion strategy is proposed to couple visual embeddings from multiple scales. This enriches the detailed representation of medical images and thus improves the discrimination of unknown diseases. Moreover, a cross-scale hard pseudo-OOD sample generation strategy is proposed to benefit OOD detection maximally. Experimental evaluations on three public medical datasets support that the proposed framework achieves superior OOD detection performance compared to existing methods. The source code is available at https://openi.pcl.ac.cn/OpenMedIA/HVL.
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