Automatic prostate MR image segmentation is needed to help doctors achieve fast and accurate disease diagnosis and treatment planning. Deep learning (DL) has shown promising achievements. However, DL models often face challenges in applications when there are large discrepancies between the training (source domain) and test (target domain) data. Here we propose a novel unsupervised domain adaptation method to address this issue without utilizing any target domain labels. Our method introduces two models trained in parallel to filter and correct the pseudo-labels generated for the target domain training data and thus, achieves substantially improved segmentation results on the test data.
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