Abstract

Multi-label chest X-ray (CXR) diagnosis remains challenging due to the
high variability of lesion regions and the complex inter-disease relationships. Existing algorithms often rely on single-scale features and static label cooccurrence, limiting their ability to capture subtle lesions and dynamic label dependencies. This paper proposes a lesion region awareness and adaptive label-relation graph algorithm for multi-label CXR image classification. Firstly, the multi-scale feature-aware semantic learning method is proposed, which localizes disease regions of interest within features of different image scales, thereby extracting representations rich in the contextual information of the disease. Secondly, the adaptive label relation graph method is proposed,
which dynamically models the dependencies among diseases for each
sample and propagates discriminative features containing disease relations. Finally, the class-level feature enhancement method is proposed. Through the intra-class supervised contrastive learning strategy, the aggregation of disease-specific features is enhanced, further improving the discriminative ability and robustness of the algorithm. Experimental results on the CXR dataset demonstrate that the proposed algorithm outperforms state-of-the-art algorithms, verifying its effectiveness in multi-label chest disease classification.