DiffMCG: A diffusion model with mask-conditioned guiding module for medical image classification

Abstract

Diffusion models have application potential in medical image classification tasks due to their effectiveness in eliminating unexpected noise and perturbations from medical images. However, existing diffusion models for medical image classification utilize image features as the condition guiding diffusion model denoising, neglecting the most critical structured semantic information within medical images—namely, the mask of the lesion region. This results in suboptimal denoising performance from diffusion models, consequently impairing classification performance. To address this issue, we propose a diffusion model with the mask-conditioned guiding module called DiffMCG. Specifically, we introduce the Mask-Conditioned Guiding (MCG) module that concurrently extracts features from the medical image and its corresponding mask. Secondly, we design a U-Net denoising network based on the multi-layer perceptron (MLP) that is tailored for low-dimensional vector data and performs denoising tasks within the category label space. Furthermore, we introduce the MMD regularization constraint loss to establish a distributional relationship between the image prediction distribution, mask prediction distribution, and ground-truth label distribution within the label prediction space. This ensures the consistency of multimodal information during the diffusion process. Through analysis of comparative and ablation experiments, we validate the advantages of the MCG module in medical image classification, providing technical support for precision medical diagnostics.