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Deep learning-based multi-class COVID-19 classification with x-ray images.

, , , , and . Medical Imaging: Image-Guided Procedures, volume 11598 of SPIE Proceedings, SPIE, (2021)

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A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods., , , and . CoRR, (2023)Automatic atlas-based building of point distribution model for segmentation of anatomical structures from brain MRI., , and . ISSPA (2), page 629-630. IEEE, (2003)0-7803-7946-2.Possibilistic-clustering-based MR brain image segmentation with accurate initialization., , , , and . VCIP, volume 5308 of SPIE Proceedings, SPIE, (2004)Histogram-Based Generation Method of Membership Function for Extracting Features of Brain Tissues on MRI Images., , , , , and . FSKD (1), volume 3613 of Lecture Notes in Computer Science, page 189-194. Springer, (2005)Modelling and Tracking of Deformable Structures in Medical Images., , and . ICIG (2), volume 9218 of Lecture Notes in Computer Science, page 475-490. Springer, (2015)Signal Separation with A Priori Knowledge Using Sparse Representation., and . Advances in Heuristic Signal Processing and Applications, Springer, (2013)SegTHOR: Segmentation of Thoracic Organs at Risk in CT images., , , and . CoRR, (2019)Deep Disentangled Representation Learning of Pet Images for Lymphoma Outcome Prediction., , , , and . ISBI, page 1-4. IEEE, (2020)Cerebral magnetic resonance image segmentation using fuzzy Markov Random Fields., , , , and . ISBI, page 237-240. IEEE, (2002)Prediction of Head-Neck Cancer Recurrence from Pet/CT Images with Havrda-Charvat Entropy., , , , , and . IPTA, page 1-5. IEEE, (2023)