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

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Xiangrui Zeng, Nibras Abdullah, Putra Sumari
{"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":null,"pages":null},"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.

自监督学习框架在医学图像分析中的应用:回顾与总结。
医学影像数据集的人工标注耗费大量人力,而且容易产生偏差。此外,图像数据的积累速度大大超过了人工标注的速度,这对机器学习的进步,尤其是监督学习领域的进步提出了挑战。自我监督学习是一个新兴领域,它利用未标注数据进行训练,从而避免了大量人工标注的需要。这种学习模式通过借口任务生成合成伪标签,迫使网络以一种伪监督的方式获取图像表征,然后利用有限的注释数据集进行微调,以提高性能。本综述首先概述了自监督学习的普遍类型和进展,然后对 2018 年至 2024 年 9 月期间医学影像领域的方法进行了详尽、系统的研究。综述涵盖一系列医学影像模式,包括 CT、MRI、X 射线、组织学和超声波。它涉及分类、定位、分割、减少误判、提高模型性能和增强图像质量等具体任务。分析表明,相关研究的数量从高到低依次为 CT 和 MRI,其次是 X 射线、组织学和超声波。除 CT 和 MRI 外,更多的研究侧重于对比学习方法,而不是生成学习方法。MRI/Ultrasound 分类和所有图像类型分割的性能仍有进一步探索的空间。总体而言,本综述可为医学专业人员提供概念指导,帮助他们将自我监督学习与研究相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
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
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信