A Self-Supervised Learning Frameworks for MRI-Based Medical Image Classification:

In recent years Self Supervised learning has evolved as a framework that eliminates the bottle neck of dependence on labelled data for conventional supervised learning paradigms. It has opened entirely new possibilities by removing dependence on the annotated data, which is particularly useful for medical image classification problems as finding the annotated data is scarce in this domain. In this paper we have gathered and presented a structured view of the usage of SSL framework and techniques for MRI-based medical image classification. We have consolidated a detailed review of this modality that can be practical for future research and experimentation.