Dual consistent pseudo label generation for multi-source domain adaptation without source data for medical image segmentation - Cai, Binke; Ma, Liyan; Sun, Yan (Frontiers in Neuroscience, 2023)
Unsupervised Domain Adaptation (UDA) aims to adapt a model learned from the source domain to the target domain. Thus, the model can obtain transferable knowledge even in target domain that does not have ground truth in this way. In medical image segmentation scenarios, there exist diverse data distributions caused by intensity inhomogeneities and shape variabilities. But the multi source data may not be freely accessible, especially these medical images with patient identity information. To tackle this issue, we propose a new Multi Source and Source Free (MSSF) application scenario and a novel domain adaptation framework where in training stage we only get access to the well-trained source domain segmentation models without source data. First, we proposed a new Dual Consistency Constraint which uses domain-intra and domain-inter consistency to filter those predictions agreed by each individual domain expert and all domain experts. It can serve as a high quality pseudo label generation method and produce correct supervised signals for target domain supervised learning. Next, we design a progressive entropy loss minimization method to minimize the class-inter distance of features, which is beneficial to enhance domain-intra and domain-inter consistency in turn. Extensive experiments are performed for retinal vessel segmentation under Multi Source and Source Free (MSSF) condition and our approach produce impressive performance.