Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease classification and segmentation. Recent mVLCL methods attempt to align image sub-regions and the report keywords as local-matchings. However, these methods aggregate all local-matchings via simple pooling operations while ignoring the inherent relations between them. These methods therefore fail to reason between local-matchings that are semantically related, e.g., local-matchings that correspond to the disease word and the location word (semantic-relations), and also fail to differentiate such clinically important local-matchings from others that correspond to less meaningful words, e.g., conjunction words (importance-relations). Hence, we propose a mVLCL method that models the inter-matching relations between local-matchings via a relation-enhanced contrastive learning framework (RECLF). In RECLF, we introduce a semantic-relation reasoning module (SRM) and an importance-relation reasoning module (IRM) to enable more fine-grained report supervision for image representation learning. We evaluated our method using four public benchmark datasets on four downstream tasks, including segmentation, zero-shot classification, supervised classification, and cross-modal retrieval. Our results demonstrated the superiority of our RECLF over the state-of-the-art mVLCL methods with consistent improvements across single-modal and cross-modal tasks. These results suggest that our RECLF, by modelling the inter-matching relations, can learn improved medical image representations with better generalization capabilities.

翻译：医学图像表示可以通过医学视觉-语言对比学习（mVLCL）实现，其中医学影像报告通过图文对齐作为弱监督信号。这些学习的图像表示可以迁移并促进多种下游医学视觉任务，如疾病分类和分割。最近的mVLCL方法尝试将图像子区域与报告关键词进行局部匹配对齐。然而，这些方法通过简单池化操作聚合所有局部匹配，忽略了它们之间的内在关系。因此，这些方法无法推理语义相关的局部匹配（例如，对应疾病词和位置词的局部匹配之间的语义关系），也无法将这类临床重要的局部匹配与对应无意义词汇（如连词）的局部匹配区分开（重要性关系）。为此，我们提出一种通过关系增强对比学习框架（RECLF）对局部匹配间的匹配间关系进行建模的mVLCL方法。在RECLF中，我们引入语义关系推理模块（SRM）和重要性关系推理模块（IRM），以提供更细粒度的报告监督用于图像表示学习。我们在四个下游任务的四个公开基准数据集上评估了方法，包括分割、零样本分类、监督分类和跨模态检索。结果表明，RECLF在单模态和跨模态任务中持续优于最先进的mVLCL方法。这些结果证明，通过建模匹配间关系，RECLF能够学习具有更好泛化能力的改进医学图像表示。