Abstract
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 is available at https://github.com/ustlsh/IOIS_imbalance.
S. Li and Y. Lin—Equal contribution
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Acknowledgments
This research was supported by National Natural Science Foundation of China/HKSAR Research Grants Council Joint Research Scheme under Grant N_HKUST627/20 and by Nanhai People’s Government of Foshan under Project FSNH 22EG05/RG203.
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Li, S., Lin, Y., Chen, H., Cheng, KT. (2024). Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15005. Springer, Cham. https://doi.org/10.1007/978-3-031-72086-4_35
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DOI: https://doi.org/10.1007/978-3-031-72086-4_35
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