Piffer Stefano, Ubaldi Leonardo, Tangaro Sabina, Retico Alessandra, Talamonti Cinzia
Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy.
National Institute for Nuclear Physics (INFN), Florence Division, Florence, Italy.
Prog Biomed Eng (Bristol). 2024 Jun 17;6(3). doi: 10.1088/2516-1091/ad525b.
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.
尽管医学成像在人工智能研究中越来越受到关注，但训练模型需要大量数据。在这个领域，可用的数据集有限，因为收集新数据要么不可行，要么需要耗费大量资源。研究人员面临着数据集小的问题，不得不采用一些技巧来对抗过拟合。截至2022年7月31日，从PubMed检索到147篇经同行评审的英文文章，由两名独立评审员对文章进行评估。我们遵循系统评价和Meta分析的首选报告项目（PRISMA）指南进行论文筛选，77项研究被认为符合本综述的范围。使用TRIPOD声明（个体预后或诊断的多变量预测模型的透明报告）评估对报告标准的遵守情况。为了解决小数据问题，分别在75%、69%和14%的案例中应用了迁移学习技术、基本数据增强和生成对抗网络。鉴于数据稀缺和任务难度，超过60%的作者进行了二元分类。关于可推广性，只有四项研究明确指出对开发的模型进行了外部验证。对所有数据集和代码的完全访问受到严格限制（超过80%的研究中无法获得）。对报告标准的遵守情况不理想（37项TRIPOD项目中有13项的遵守率低于50%）。本综述的目的是对处理小尺寸医学图像样本的最新进展进行全面综述。同时也支持提高出版物的透明度和质量以及遵循现有的报告标准。
