# Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation

arXiv: https://arxiv.org/abs/2411.09434 · HTML: https://mlsanity.com/paper/2411.09434/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- First posted on arXiv 12 Nov 2024.

## Placed in

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

## Built on by (most cited first, 1 of 1)

- [Explainability in Generative Medical Diffusion Models: A Faithfulness-Based Analysis on MRI Synthesis](https://mlsanity.com/paper/2602.09781/) (2026)

## Abstract (arXiv)

We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- insufficient for standard training, and critical nature of the applications that require high performance, confidence, and explainability of the models. In this work, we propose to tackle those challenges with a single model that combines standard classification with a diffusion-based generative task in a single shared parametrisation. By sharing representations, our model effectively learns from both labeled and unlabeled data while at the same time providing accurate explanations through counterfactual examples. In our experiments, we show that our Mediffusion achieves results comparable to recent semi-supervised methods while providing more reliable and precise explanations.