Self-supervised learning framework application for medical image analysis: a review and summary

Self-supervised learning framework application for medical image analysis: a review and summary

Blog Article

Abstract Manual annotation of medical image datasets is labor-intensive and prone to biases.Moreover, the rate at which image data accumulates significantly outpaces the speed of manual annotation, posing a challenge to the advancement of machine learning, particularly in the realm of supervised learning.Self-supervised learning is an emerging field that capitalizes on unlabeled data for training, thereby circumventing the need for extensive manual labeling.This learning paradigm generates synthetic pseudo-labels through pretext tasks, compelling the network to acquire image representations in a pseudo-supervised manner and subsequently fine-tuning with a limited set of annotated data to achieve enhanced performance.

This puffy spa headband review begins with an overview of prevalent types and advancements in self-supervised learning, followed by an exhaustive and systematic examination of methodologies within the medical imaging domain from 2018 to September 2024.The review encompasses a range of medical image modalities, including CT, MRI, X-ray, Histology, and Ultrasound.It addresses specific tasks, such as Classification, Localization, Segmentation, Reduction here of False Positives, Improvement of Model Performance, and Enhancement of Image Quality.The analysis reveals a descending order in the volume of related studies, with CT and MRI leading the list, followed by X-ray, Histology, and Ultrasound.

Except for CT and MRI, there is a greater prevalence of studies focusing on contrastive learning methods over generative learning approaches.The performance of MRI/Ultrasound classification and all image types segmentation still has room for further exploration.Generally, this review can provide conceptual guidance for medical professionals to combine self-supervised learning with their research.