Debiased Curriculum Adaptation for Safe Transfer Learning in Chest X-Ray Classification

Chest X-ray classification is extensively utilized within the field of medical image analysis. However, manually labeling chest X-ray images is time-consuming and costly. Domain adaptation, which is designed to transfer knowledge from related domains, could offer a promising solution. Existing methods employ feature adaptation or self-training for knowledge transfer. Nonetheless, negative transfer is observed due to the entanglement of class imbalance and distribution shift in chest X-ray classification. In this paper, we propose Debiased Curriculum Adaptation framework to mitigate negative transfer in two aspects: (1) Curriculum Adaptation, which is designed to transfer knowledge in an easy-to-hard way, is proposed to alleviate confirmation bias in self-training. (2) Spectral Debiasing is introduced to harmonize the feature space between the source and target domains, as well as balance the feature space of positive and negative samples. Extensive experiments on 72 transfer tasks (including 6 diseases and 4 domains) demonstrate our superiority over state-of-the-art methods. In comparison to advanced methods, our approach effectively mitigates negative transfer, ensuring safe knowledge transfer.