# DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification

arXiv: https://arxiv.org/abs/2303.10610 · HTML: https://mlsanity.com/paper/2303.10610/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)
- [Generative Moment Matching Networks](https://mlsanity.com/paper/1502.02761/) (2015)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [Conditional Image Generation with Score-Based Diffusion Models](https://mlsanity.com/paper/2111.13606/) (2021)
- [InfoVAE: Information Maximizing Variational Autoencoders](https://mlsanity.com/paper/1706.02262/) (2017)
- [CARD: Classification and Regression Diffusion Models](https://mlsanity.com/paper/2206.07275/) (2022)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [High-Fidelity Guided Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2211.17084/) (2022)
- [ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification](https://mlsanity.com/paper/2209.00183/) (2022)
- [Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications](https://mlsanity.com/paper/2007.09979/) (2020)
- [A Deep Multi-task Learning Approach to Skin Lesion Classification](https://mlsanity.com/paper/1812.03527/) (2018)

## Built on by (most cited first, 30 of 32)

- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal](https://mlsanity.com/paper/2403.07684/) (2024)
- [Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization](https://mlsanity.com/paper/2410.07901/) (2024)
- [SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Models](https://mlsanity.com/paper/2412.02332/) (2024)
- [Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning](https://mlsanity.com/paper/2409.07238/) (2024)
- [A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models](https://mlsanity.com/paper/2308.16355/) (2023)
- [Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding](https://mlsanity.com/paper/2311.02991/) (2023)
- [Diff-MTS: Temporal-Augmented Conditional Diffusion-based AIGC for Industrial Time Series Towards the Large Model Era](https://mlsanity.com/paper/2407.11501/) (2024)
- [An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis](https://mlsanity.com/paper/2407.03018/) (2024)
- [RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models](https://mlsanity.com/paper/2410.00307/) (2024)
- [This Probably Looks Exactly Like That: An Invertible Prototypical Network](https://mlsanity.com/paper/2407.12200/) (2024)
- [Volumetric Conditioning Module to Control Pretrained Diffusion Models for 3D Medical Images](https://mlsanity.com/paper/2410.21826/) (2024)
- [Diffusion models applied to skin and oral cancer classification](https://mlsanity.com/paper/2504.00026/) (2025)
- [SEMC: Structure-Enhanced Mixture-of-Experts Contrastive Learning for Ultrasound Standard Plane Recognition](https://mlsanity.com/paper/2511.12559/) (2025)
- [SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector](https://mlsanity.com/paper/2312.06187/) (2023)
- [Parameterized Diffusion Optimization enabled Autoregressive Ordinal Regression for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2507.04978/) (2025)
- [WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification](https://mlsanity.com/paper/2401.13998/) (2024)
- [DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication](https://mlsanity.com/paper/2402.02060/) (2024)
- [Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.19699/) (2025)
- [Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2505.07866/) (2025)
- [HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors](https://mlsanity.com/paper/2507.22530/) (2025)
- [Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models](https://mlsanity.com/paper/2508.07903/) (2025)
- [CECT-Mamba: a Hierarchical Contrast-enhanced-aware Model for Pancreatic Tumor Subtyping from Multi-phase CECT](https://mlsanity.com/paper/2509.12777/) (2025)
- [USB: Unified Synthetic Brain Framework for Bidirectional Pathology-Healthy Generation and Editing](https://mlsanity.com/paper/2512.00269/) (2025)
- [Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model](https://mlsanity.com/paper/2606.01698/) (2026)
- [LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion](https://mlsanity.com/paper/2409.01317/) (2024)
- [Anisotropic Diffusion Probabilistic Model for Imbalanced Image Classification](https://mlsanity.com/paper/2409.14313/) (2024)
- [D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification](https://mlsanity.com/paper/2411.11087/) (2024)
- [BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet](https://mlsanity.com/paper/2511.12853/) (2025)
- [A Study on Real-time Object Detection using Deep Learning](https://mlsanity.com/paper/2602.15926/) (2026)

## Abstract (arXiv)

Diffusion Probabilistic Models have recently shown remarkable performance in generative image modeling, attracting significant attention in the computer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medical image classification by eliminating unexpected noise and perturbations in medical images and robustly capturing semantic representation. To achieve this goal, we devise a dual conditional guidance strategy that conditions each diffusion step with multiple granularities to improve step-wise regional attention. Furthermore, we propose learning the mutual information in each granularity by enforcing Maximum-Mean Discrepancy regularization during the diffusion forward process. We evaluate the effectiveness of our DiffMIC on three medical classification tasks with different image modalities, including placental maturity grading on ultrasound images, skin lesion classification using dermatoscopic images, and diabetic retinopathy grading using fundus images. Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin, indicating the universality and effectiveness of the proposed model. Our code will be publicly available at https://github.com/scott-yjyang/DiffMIC.