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Medical image classification poses unique challenges due to the long-taileddistribution of diseases, the co-occurrence of diagnostic findings, and themultiple views available for each study or patient. This paper introduces oursolution to the ICCV CVAMD 2023 Shared Task on CXR-LT: Multi-Label Long-TailedClassification on Chest X-Rays. Our approach introduces CheXFusion, atransformer-based fusion module incorporating multi-view images. The fusionmodule, guided by self-attention and cross-attention mechanisms, efficientlyaggregates multi-view features while considering label co-occurrence.Furthermore, we explore data balancing and self-training methods to optimizethe model's performance. Our solution achieves state-of-the-art results with0.372 mAP in the MIMIC-CXR test set, securing 1st place in the competition. Oursuccess in the task underscores the significance of considering multi-viewsettings, class imbalance, and label co-occurrence in medical imageclassification. Public code is available athttps://github.com/dongkyuk/CXR-LT-public-solution

CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification