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Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification
March 14, 2024, 4:46 a.m. | Shuhan Li, Yi Lin, Hao Chen, Kwang-Ting Cheng
cs.CV updates on arXiv.org arxiv.org
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) …
abstract arxiv challenges classification collection cs.cv data data collection datasets diagnosis diffusion diffusion model disease diseases face however image iterative medical paper robust sample synthesis treatment type types via
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