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Vision-Language Pre-training (VLP) that utilizes the multi-modal informationto promote the training efficiency and effectiveness, has achieved greatsuccess in vision recognition of natural domains and shown promise in medicalimaging diagnosis for the Chest X-Rays (CXRs). However, current works mainlypay attention to the exploration on single dataset of CXRs, which locks thepotential of this powerful paradigm on larger hybrid of multi-source CXRsdatasets. We identify that although blending samples from the diverse sourcesoffers the advantages to improve the model generalization, it is stillchallenging to maintain the consistent superiority for the task of each sourcedue to the existing heterogeneity among sources. To handle this dilemma, wedesign a Conquer-and-Divide pre-training framework, termed as UniChest, aimingto make full use of the collaboration benefit of multiple sources of CXRs whilereducing the negative influence of the source heterogeneity. Specially, the``Conquer" stage in UniChest encourages the model to sufficiently capturemulti-source common patterns, and the ``Divide" stage helps squeezepersonalized patterns into different small experts (query networks). We conductthorough experiments on many benchmarks, e.g., ChestX-ray14, CheXpert,Vindr-CXR, Shenzhen, Open-I and SIIM-ACR Pneumothorax, verifying theeffectiveness of UniChest over a range of baselines, and release our codes andpre-training models at https://github.com/Elfenreigen/UniChest.

UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification