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Tackling the small data problem in medical image classification with artificial intelligence: a systematic review

计算机科学 过度拟合 概化理论 范围(计算机科学) 稀缺 人工智能 机器学习 数据挖掘 数据科学 心理学 人工神经网络 发展心理学 经济 微观经济学 程序设计语言
作者
Stefano Piffer,Leonardo Ubaldi,Sabina Tangaro,Alessandra Retico,C. Talamonti
出处
期刊:Progress in Biomedical Engineering 卷期号:6 (3): 032001-032001 被引量:2
标识
DOI:10.1088/2516-1091/ad525b
摘要

Abstract Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources. Researchers are facing with the problem of small datasets and have to apply tricks to fight overfitting. 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31 July 2022 and articles were assessed by two independent reviewers. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyse (PRISMA) guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review. Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). To solve the small data issue transfer learning technique, basic data augmentation and generative adversarial network were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a binary classification given the data scarcity and the difficulty of the tasks. Concerning generalizability, only four studies explicitly stated an external validation of the developed model was carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD items). The goal of this review is to provide a comprehensive survey of recent advancements in dealing with small medical images samples size. Transparency and improve quality in publications as well as follow existing reporting standards are also supported.
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