text: 452,20,1299306215491670020,933713808018862082,Cian Scannell,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&Ms challenge. @achir76 @mitkoveta <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 <LINK> This ensures that the domain of the input cannot be recovered from the features learned by the U-Net. It 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 <LINK>,https://arxiv.org/abs/2008.11776,"Cine cardiac magnetic resonance (CMR) has become the gold standard for the non-invasive evaluation of cardiac function. In particular, it allows the accurate quantification of functional parameters including the chamber volumes and ejection fraction. Deep learning has shown the potential to automate the requisite cardiac structure segmentation. However, the lack of robustness of deep learning models has hindered their widespread clinical adoption. Due to differences in the data characteristics, neural networks trained on data from a specific scanner are not guaranteed to generalise well to data acquired at a different centre or with a different scanner. In this work, we propose a principled solution to the problem of this domain shift. Domain-adversarial learning is used to train a domain-invariant 2D U-Net using labelled and unlabelled data. This approach is evaluated on both seen and unseen domains from the M\&Ms challenge dataset and the domain-adversarial approach shows improved performance as compared to standard training. Additionally, we show that the domain information cannot be recovered from the learned features. ","Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and