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Diffusion Probabilistic Models have recently shown remarkable performance ingenerative image modeling, attracting significant attention in the computervision community. However, while a substantial amount of diffusion-basedresearch has focused on generative tasks, few studies have applied diffusionmodels to general medical image classification. In this paper, we propose thefirst diffusion-based model (named DiffMIC) to address general medical imageclassification by eliminating unexpected noise and perturbations in medicalimages and robustly capturing semantic representation. To achieve this goal, wedevise a dual conditional guidance strategy that conditions each diffusion stepwith multiple granularities to improve step-wise regional attention.Furthermore, we propose learning the mutual information in each granularity byenforcing Maximum-Mean Discrepancy regularization during the diffusion forwardprocess. We evaluate the effectiveness of our DiffMIC on three medicalclassification tasks with different image modalities, including placentalmaturity grading on ultrasound images, skin lesion classification usingdermatoscopic images, and diabetic retinopathy grading using fundus images. Ourexperimental results demonstrate that DiffMIC outperforms state-of-the-artmethods by a significant margin, indicating the universality and effectivenessof the proposed model. Our code will be publicly available athttps://github.com/scott-yjyang/DiffMIC.

DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification