Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and inherent imbalanced distributions, due to difficulties in data collection and variations in disease prevalence across different types. In this paper, we introduce an Iterative Online Image Synthesis (IOIS) framework to address the class imbalance problem in medical image classification. Our framework incorporates two key modules, namely Online Image Synthesis (OIS) and Accuracy Adaptive Sampling (AAS), which collectively target the imbalance classification issue at both the instance level and the class level. The OIS module alleviates the data insufficiency problem by generating representative samples tailored for online training of the classifier. On the other hand, the AAS module dynamically balances the synthesized samples among various classes, targeting those with low training accuracy. To evaluate the effectiveness of our proposed method in addressing imbalanced classification, we conduct experiments on the HAM10000 and APTOS datasets. The results obtained demonstrate the superiority of our approach over state-of-the-art methods as well as the effectiveness of each component. The source code will be released upon acceptance.

翻译：准确的疾病分类对于正确的诊断和治疗至关重要。然而，由于数据采集困难以及不同疾病类型患病率的差异，医学数据集常常面临样本数量有限和固有分布不平衡的挑战。本文提出了一种迭代式在线图像合成（IOIS）框架，旨在解决医学图像分类中的类别不平衡问题。该框架包含两个关键模块，即在线图像合成（OIS）模块和精度自适应采样（AAS）模块，它们共同在实例层面和类别层面针对不平衡分类问题发挥作用。OIS模块通过生成专门用于分类器在线训练的具有代表性的样本，缓解了数据不足问题。另一方面，AAS模块通过动态平衡不同类别之间的合成样本数量，重点关注训练精度较低的类别。为了评估所提方法在解决不平衡分类问题上的有效性，我们在HAM10000和APTOS数据集上进行了实验。结果表明，我们的方法优于现有最先进方法，同时验证了各组成部分的有效性。源代码将在论文被接收后发布。