提出新自监督框架，解决医学图像数据少且不均衡问题。
A Self supervised learning framework for imbalanced medical imaging datasets
- 设计不对称多图多视角增强，应对数据稀缺与类别不平衡。
- 在11个医学数据集上验证，多个任务提升超3%准确率。
- 适合医疗影像领域研究者，尤其关注小样本与不平衡数据。
医学图像分析常面临两大挑战：标注数据量不足，以及类别分布极度不均（常见类数据多，罕见类数据极少）。自监督学习（SSL）虽能缓解数据稀缺问题，但其在医学图像分类中对类别不平衡的鲁棒性仍鲜有研究。本文提出改进的MIMV方法，引入新的增强策略构建不对称多图多视角（AMIMV）样本对，同时应对数据稀缺与类别不平衡。通过分析不同不平衡程度下的表现，评估了8种代表性SSL方法在11个医学图像数据集（MedMNIST）上的性能。实验表明，在retinaMNIST、tissueMNIST、DermaMNIST上分别提升4.25%、1.88%和3.1%。
原文摘要 · Abstract (English)
Two problems often plague medical imaging analysis: 1) Non-availability of large quantities of labeled training data, and 2) Dealing with imbalanced data, i.e., abundant data are available for frequent classes, whereas data are highly limited for the rare class. Self supervised learning (SSL) methods have been proposed to deal with the first problem to a certain extent, but the issue of investigating the robustness of SSL to imbalanced data has rarely been addressed in the domain of medical image classification. In this work, we make the following contributions: 1) The MIMV method proposed by us in an earlier work is extended with a new augmentation strategy to construct asymmetric multi-image, multi-view (AMIMV) pairs to address both data scarcity and dataset imbalance in medical image classification. 2) We carry out a data analysis to evaluate the robustness of AMIMV under varying degrees of class imbalance in medical imaging . 3) We evaluate eight representative SSL methods in 11 medical imaging datasets (MedMNIST) under long-tailed distributions and limited supervision. Our experimental results on the MedMNIST dataset show an improvement of 4.25% on retinaMNIST, 1.88% on tissueMNIST, and 3.1% on DermaMNIST.
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