text:   Multi-Disease Cardiac MR Image Segmentation",3,"['New paper online using domain-adversarial learning to train a U-Net to segment cardiac MR images that generalises well across different domains (MR scanners). Accepted at STACOM @MICCAI20 for the M&amp;Ms challenge. @achir76 @mitkoveta \n<LINK>', 'The idea (Ganin et al. JMLR 2016) is to simultaneously train the segmentation U-Net and a domain classifier to discriminate between input domains based on the internal representations of the U-Net. Then to update the weights of U-Net to maximise the loss of the domain classifier https://t.co/1b4oYmaPPv', ""This ensures that the domain of the input cannot be recovered from the features learned by the U-Net.\nIt learns features that are less dependent on domain information than conventional training (t-SNE embeddings shown) and performance doesn't drop off when applied to new domains https://t.co/HNOv31zCXO""]",20,08,812