Enhancing Medical Image Classification Using Self-Supervised Transfer Learning

the goal of medical image classification is to accurately identify and diagnose a given pathology from imaging modalities. Traditional supervised methods approach this task by labeling the imaging data in a supervised manner, which relies heavily on access to a large dataset of labeled images. This can be costly and accessible in certain scenarios. Self-supervised transfer learning seeks to tackle this deficiency by learning on large, labeled datasets from related tasks and applying those learned representations to solve the medical image classification problem. This approach has proven effective in improving performance and making the best use of limited resources. This paper reviews recent literature and techniques related to self-supervised transfer learning for medical image classification tasks, and provide an overview of the underlying principles and implications for its possible applications…