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Interpretable and Reliable Artificial Intelligence Systems for Brain Diseases.. ERCIM News, (2019)Learning the spatiotemporal variability in longitudinal shape data sets., , and . Int. J. Comput. Vis., 128 (12): 2873-2896 (2020)A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases., , , , , , , , and . Medical Imaging: Image Processing, volume 12032 of SPIE Proceedings, SPIE, (2022)Predicting PET-derived myelin content from multisequence MRI for individual longitudinal analysis in multiple sclerosis., , , , , , , and . NeuroImage, (2020)Interpretability of Machine Learning Methods Applied to Neuroimaging., , , and . CoRR, (2022)ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing., , , , , , and . Comput. Methods Programs Biomed., (2022)Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET., , , , and . CoRR, (2024)Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI., , , , , , and . BMC Medical Imaging, 24 (1): 67 (December 2024)Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review., , , , , , , , , and 7 other author(s). Medical Image Anal., (2021)Visualization approach to assess the robustness of neural networks for medical image classification., , , and . CoRR, (2019)