Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis

• URL: http://arxiv.org/abs/2510.16887v1
• Date: Sun, 19 Oct 2025 15:37:41 GMT
• Title: Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
• Authors: Nusrat Munia, Abdullah Imran,
• Abstract summary: We propose a classification-induced diffusion model, namely, Class-N-Diff, to simultaneously generate and classify dermoscopic images.<n>Our Class-N-Diff model integrates a classifier within a diffusion model to guide image generation based on its class conditions.<n>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.
• Score: 0.790660895390689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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