Recent development of Machine Learning (ML) tools for analysis of CNS tumors demonstrates great potential benefit to research and clinical practice but has been hindered by a lack of external validation. There is a critical need for open access to large individual hospital-based datasets with expert annotations. Here, we present the Yale Glioma Dataset, a database of 1,033 patients featuring annotated segmentations on FLAIR and T1 post-gadolinium, tumor grading and classification, and further clinical information. Open access of this database will support the development and validation of new AI algorithms for glioma detection and segmentation.
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Sala et al. (2024) studied this question.
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