Iterative Online Image Synthesis via Diffusion Model for Imbalanced
  Classification
Shuhan Li Yi Lin Hao Chen Kwang-Ting Cheng
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.