用分类引导扩散模型生成更真实的皮肤癌图像，提升诊断准确性
Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
- 将分类器嵌入扩散模型，实现图像生成与分类同步优化
- 生成的皮肤镜图像更真实多样，分类器准确率显著提升
- 适合医学图像合成与辅助诊断研究者使用
生成模型，尤其是扩散模型，在生成高质量合成数据（包括医学图像）方面展现出强大能力。然而，传统的类别条件生成模型在生成特定医学类别图像时往往表现不佳，限制了其在皮肤癌诊断等应用中的实用性。为解决此问题，我们提出一种分类引导的扩散模型——Class-N-Diff，可同时生成并分类皮肤镜图像。该模型将分类器嵌入扩散框架中，根据类别条件指导图像生成，从而实现更精确的类别控制，生成更具真实感和多样性的图像。此外，分类器在下游诊断任务中性能也得到提升，验证了其有效性。这种独特集成使Class-N-Diff成为增强基于扩散模型的皮肤镜图像合成质量与实用性的有力工具。代码已公开于 https://github.com/Munia03/Class-N-Diff。
原文摘要 · Abstract (English)
Generative models, especially Diffusion Models, have demonstrated remarkable capability in generating high-quality synthetic data, including medical images. However, traditional class-conditioned generative models often struggle to generate images that accurately represent specific medical categories, limiting their usefulness for applications such as skin cancer diagnosis. To address this problem, we propose a classification-induced diffusion model, namely, Class-N-Diff, to simultaneously generate and classify dermoscopic images. Our Class-N-Diff model integrates a classifier within a diffusion model to guide image generation based on its class conditions. Thus, the model has better control over class-conditioned image synthesis, resulting in more realistic and diverse images. Additionally, the classifier demonstrates improved performance, highlighting its effectiveness for downstream diagnostic tasks. This unique integration in our Class-N-Diff makes it a robust tool for enhancing the quality and utility of diffusion model-based synthetic dermoscopic image generation. Our code is available at https://github.com/Munia03/Class-N-Diff.
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