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Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening., , , , , , , , , and . CoRR, (2022)Automated femur segmentation from computed tomography images using a deep neural network., , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 11600 of SPIE Proceedings, SPIE, (2021)Structural covariability hubs in old age., , , , and . NeuroImage, (2019)Deep-learning-based audio-visual speech enhancement in presence of Lombard effect., , , and . Speech Commun., (2019)Automated femur segmentation from computed tomography images using a deep neural network., , , , , and . CoRR, (2021)The AGES-Reykjavik study atlases: Non-linear multi-spectral template and atlases for studies of the ageing brain., , , , , , , , , and . Medical Image Anal., (2017)Localized measures of callosal atrophy are associated with late-life hypertension: AGES-Reykjavik Study., , , , , , , , , and 1 other author(s). NeuroImage, 43 (3): 489-496 (2008)Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder., , , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 109491H. SPIE, (2019)Modeling text with generalizable Gaussian mixtures., , , , , and . ICASSP, page 3494-3497. IEEE, (2000)Outlier estimation and detection application to skin lesion classification., , , , and . ICASSP, page 1049-1052. IEEE, (2002)