医疗预测问题中的噪声标签深度学习:范围综述。

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yishu Wei, Yu Deng, Cong Sun, Mingquan Lin, Hongmei Jiang, Yifan Peng
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引用次数: 0

摘要

目的:医学研究面临着由专家间差异和机器提取标签等因素造成的标签噪声带来的巨大挑战。尽管如此,标签噪声管理的应用仍然有限,标签噪声在很大程度上被忽视。为此,亟需对问题空间进行范围界定。本范围综述旨在全面综述基于深度学习的医疗预测问题中的标签噪声管理,包括标签噪声检测、标签噪声处理和评估。涉及标签不确定性的研究也包括在内:我们的范围界定综述遵循系统综述和荟萃分析首选报告项目(PRISMA)指南。我们检索了 4 个数据库,包括 PubMed、IEEE Xplore、Google Scholar 和 Semantic Scholar。检索词包括 "噪声标签和医疗/保健/临床"、"不确定性和医疗/保健/临床 "以及 "噪声和医疗/保健/临床":2016年至2023年间共有60篇论文符合纳入标准。研究了医学研究中的一系列实际问题。这些问题包括标签噪声的来源、标签噪声的影响、标签噪声的检测、标签噪声处理技术及其评估。对标签噪声检测方法和处理技术进行了分类:从方法论的角度来看,我们发现医学界已经跟上了更广泛的深度学习界的步伐,因为大多数技术已经在医学数据上进行了评估。我们建议将标签噪声视为医学研究的一个标准要素,即使不是专门处理噪声标签。初始实验可以从易于实施的方法入手,例如抗噪损失函数、加权和课程学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning with noisy labels in medical prediction problems: a scoping review.

Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included.

Methods: Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical/healthcare/clinical," "uncertainty AND medical/healthcare/clinical," and "noise AND medical/healthcare/clinical."

Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided.

Discussion: From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.

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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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