Feb. 15, 2024, 5:42 a.m. | Marina Manso Jimeno, Keerthi Sravan Ravi, Maggie Fung, John Thomas Vaughan, Jr., Sairam Geethanath

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08749v1 Announce Type: cross
Abstract: Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning model to detect rigid motion in T1-weighted brain images. We leveraged a 2D CNN for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. …

abstract acquisition analysis artificial artificial intelligence arxiv assessment automated brain cs.cv cs.lg data data quality deep learning detection explainable artificial intelligence images intelligence interpretation mri quality study success type

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