Statistical distribution alignment methods for domain adaptation assume similar class distributions across domains, but this assumption cannot always be guaranteed in medical imaging data. This research investigates the effect of cross-domain class imbalance on statistical distri
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Statistical distribution alignment methods for domain adaptation assume similar class distributions across domains, but this assumption cannot always be guaranteed in medical imaging data. This research investigates the effect of cross-domain class imbalance on statistical distribution alignment in unsupervised domain adaptation for medical image classification. Our experiments demonstrate that statistical distribution alignment using MMD performs reliably under mild domain shifts but struggles when both severe cross-domain class imbalance and complex domain shifts are present. To address this, we implement class-conditioned domain alignment with a new weighted minibatch sampling method. Under conditions of extreme domain shift and severe cross-domain class imbalance, combining statistical distribution alignment with more complex sampling strategies results in small improvements compared to alignment with random sampling, suggesting that class-conditioned distribution alignment offers limited practical benefits. The model appears robust to label noise, but since the performance gains are tiny, the choice of sampling strategy could have limited influence on overall performance. In our experiments, we employ the CHECK and OAI hip X-ray datasets to investigate binary osteoarthritis classification under varying levels of domain shift and cross-domain class imbalance.
