Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Sequence; ClassificationFor automatic sequence-type classification of brain MRI, we developed the self-supervised machine learning (ML) algorithm, named ImageSort-net, using a rule-based labeling system based on metadata of Digital Imaging and Communications in Medicine (DICOM) image files. Our rule-based labeling system and ImageSort-net showed high classification performance to predict brain MRI sequence type. ImageSort-net showed reliable performance by appending a new dataset to an existing dataset and without human labeling of the whole dataset. This result indicates that sustainable self-learning ML algorithms using the rule-based virtual label in the new datasets are feasible.
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