33 Victor Campello Computer Vision News Illustration of the effect of spatial and intensity-based data augmentation applied to a contrastenhanced cardiac MRI scan. to be overcome before these methods are used in the daily practice. Victor focuses on one of these important challenges in his thesis: the generalization of models to unseen domains independently of other factors, such as the scanner manufacturer, the scanning protocol, the sample size or the image quality. In his thesis, Victor established a collaboration with clinical researchers from six different centres from Spain, Germany and Canada to assemble a large multicenter dataset: the M&Ms Dataset. These data were used in the M&Ms Challenge, organized at MICCAI 2020 in Perú (virtual edition), with the aim of comparing and analysing different techniques proposed by the participating teams to bridge the domain gap. The results highlighted the importance of well-established frameworks and extensive data augmentation. The second contribution focused on model generalization on contrastenhanced imaging, where the variability in image appearance across centers is larger due to the injection of a contrast agent and the disparities in the time elapsed between the contrast injection and the scan. Victor and his colleagues showed that extensive data augmentation (shown in Figure 2) is very important for generalization and that model fine-tuning can reach or even surpass the performance of multi-centre models. In the final contribution of the thesis, Victor and his colleagues investigated how to harmonize images and features from multiple centres for an improved diagnostic accuracy on unseen domains. They showed that histogram matchingbased harmonisation results in image features (radiomics) that are more generalizable across centres.
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