Active consistency network for multi-source domain generalization in brain tumor segmentation

Recent advances in deep learning for brain tumor segmentation demonstrate good performance when the training and test data share the same distribution. However, medical images from different medical institutions often face distribution shifts in clinical applications. To deal with domain shift in multi-source domain generalization, we propose Active Consistency Network (ACN) with a single encoder and dual decoders to learn domain invariant features. A causal feature learning strategy and uncertainty-based refinery strategy are proposed to obtain reliable soft labels of reliable samples (Active Consistency Labels, ACL). Active consistency labels remove the noisy and unreliable samples for model consistency. Causality-based Random Masking (CRM) is proposed to obtain more stable causal features by adversarial learning. Causal features help achieve stable uncertainty estimation. Besides, two decoders are supervised by the active consistency labels learned from each other's decoders, which can help decoders learn domain invariant features. Experimental results show that ACN utilizes causal feature learning and active consistency labels improving segmentation performance on multi-institutional brain tumor segmentation and achieving state-of-the-art performance compared to other domain generalization methods.