# Diffusion-Based Approaches in Medical Image Generation and Analysis

arXiv: https://arxiv.org/abs/2412.16860 · HTML: https://mlsanity.com/paper/2412.16860/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Placed in

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

## Builds on (most cited first, 12 of 12)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Hyperspherical Variational Auto-Encoders](https://mlsanity.com/paper/1804.00891/) (2018)
- [UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.05358/) (2021)
- [Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser](https://mlsanity.com/paper/2007.13640/) (2020)
- [Stabilizing Generative Adversarial Networks: A Survey](https://mlsanity.com/paper/1910.00927/) (2019)
- [Unified Representation Learning for Efficient Medical Image Analysis](https://mlsanity.com/paper/2006.11223/) (2020)
- [A Novel Framework for Selection of GANs for an Application](https://mlsanity.com/paper/2002.08641/) (2020)
- [Memory-efficient array redistribution through portable collective communication](https://mlsanity.com/paper/2112.01075/) (2021)

## Built on by (most cited first, 4 of 4)

- [LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized Text-Guided Image Editing](https://mlsanity.com/paper/2503.21541/) (2025)
- [Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development](https://mlsanity.com/paper/2510.12253/) (2025)
- [Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models](https://mlsanity.com/paper/2504.00189/) (2025)
- [Uncertainty-guided Generation of Dark-field Radiographs](https://mlsanity.com/paper/2601.15859/) (2026)

## Abstract (arXiv)

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-generated samples can help CNN models perform comparably to those trained on original datasets, reliance on patient-specific data for training CNNs might be reduced. In this study, we investigated the effectiveness of diffusion models for generating synthetic medical images to train CNNs in three domains: Brain Tumor MRI, Acute Lymphoblastic Leukemia (ALL), and SARS-CoV-2 CT scans. A diffusion model was trained to generate synthetic datasets for each domain. Pre-trained CNN architectures were then trained on these synthetic datasets and evaluated on unseen real data. All three datasets achieved promising classification performance using CNNs trained on synthetic data. Local Interpretable Model-Agnostic Explanations (LIME) analysis revealed that the models focused on relevant image features for classification. This study demonstrates the potential of diffusion models to generate synthetic medical images for training CNNs in medical image analysis.