{"title":"Self-supervised learning framework application for medical image analysis: a review and summary.","authors":"Xiangrui Zeng, Nibras Abdullah, Putra Sumari","doi":"10.1186/s12938-024-01299-9","DOIUrl":null,"url":null,"abstract":"<p><p>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 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 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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"107"},"PeriodicalIF":2.9000,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514943/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMedical Engineering OnLine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12938-024-01299-9","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
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 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 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.
期刊介绍:
BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering.
BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to:
Bioinformatics-
Bioinstrumentation-
Biomechanics-
Biomedical Devices & Instrumentation-
Biomedical Signal Processing-
Healthcare Information Systems-
Human Dynamics-
Neural Engineering-
Rehabilitation Engineering-
Biomaterials-
Biomedical Imaging & Image Processing-
BioMEMS and On-Chip Devices-
Bio-Micro/Nano Technologies-
Biomolecular Engineering-
Biosensors-
Cardiovascular Systems Engineering-
Cellular Engineering-
Clinical Engineering-
Computational Biology-
Drug Delivery Technologies-
Modeling Methodologies-
Nanomaterials and Nanotechnology in Biomedicine-
Respiratory Systems Engineering-
Robotics in Medicine-
Systems and Synthetic Biology-
Systems Biology-
Telemedicine/Smartphone Applications in Medicine-
Therapeutic Systems, Devices and Technologies-
Tissue Engineering