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Abstract #4648

Development of a self-supervised machine learning algorithm for automatic MRI sequence-type classification

Seongwon Na1, Yousun Ko2, Su Jung Ham1, Mi-Hyun Kim1, Youngbin Shin1, Yu Sub Sung1, Jimi Huh3, Seung Chai Jung1, and Kyung Won Kim1
1Asan Medical Center, Seoul, Korea, Republic of, 2University of Ulsan College of Medicine, Seoul, Korea, Republic of, 3Ajou University Hospital, Suwon, Korea, Republic of

Synopsis

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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Keywords