Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge — mentioned by shoo

Mon, 11 Oct 2021 03:18:56 GMT

> All the imaging datasets have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced board-certified neuro-radiologists. Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edematous/invaded tissue (ED — label 2), and the necrotic tumor core (NCR — label 1), as described both in the BraTS 2012-2013 TMI paper [1] and in the latest BraTS summarizing paper [2]. The ground truth data were created after their pre-processing, i.e., co-registered to the same anatomical template, interpolated…

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge · HackerPapers discussions

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge — mentioned by shoo