1. Toward quantifying the prevalence, severity, and cost associated with patient motion during clinical MR examinations;Andre;J. Am. Coll. Radiol.,2015
2. Retrospective correction of rigid and non-rigid mr motion artifacts using gans;Armanious,2019
3. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features;Bakas;Sci. Data,2017
4. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge;Bakas,2018
5. Belton, N., Hagos, M.T., Lawlor, A., Curran, K.M., 2023. FewSOME: One-Class Few Shot Anomaly Detection With Siamese Networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp. 2977–2986.