Review on self supervised learning in medical image analysis

Nowadays, self-supervised learning is a popular method for analysing medical images since it annotates the unstructured data provided and uses these self-generated data labels as a foundation for subsequent model training rounds. This review paper is more focused on the recent research done in medic...

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Bibliographic Details
Published in:2023 IEEE 7th Conference on Information and Communication Technology (CICT) pp. 1 - 6
Main Authors: Kumari, Nitu, Agrawal, Sonali
Format: Conference Proceeding
Language:English
Published: IEEE 15-12-2023
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Summary:Nowadays, self-supervised learning is a popular method for analysing medical images since it annotates the unstructured data provided and uses these self-generated data labels as a foundation for subsequent model training rounds. This review paper is more focused on the recent research done in medical imaging using self-supervised learning, the major challenges of medical imaging, and the challenges of choosing the proper pretext task and data augmentation while using self-supervised learning. Finally, we address potential approaches for future studies and highlight concerns to consider while developing new self-supervised learning concepts and methodologies.
DOI:10.1109/CICT59886.2023.10455714