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.

通过关系增强的对比学习框架（RECLF），我们提出了一种可以模拟匹配间关系的医学图像表示学习方法，该方法利用医学图像报告作为弱监督，并通过图像 - 文本对齐实现。我们的结果表明，通过模拟匹配间关系，我们的 RECLF 方法可以学习到具有更好泛化能力的改进型医学图像表示。