Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
Discriminative classifiers have become a foundational tool in deep learning
for medical imaging, excelling at learning separable features of complex data
distributions. However, these models often need careful design, augmentation,
and training techniques to ensure safe and reliable deployment. Recently,
diffusion models have become synonymous with generative modeling in 2D. These
models showcase robustness across a range of tasks including natural image
classification, where classification is performed by comparing reconstruction
errors across images generated for each possible conditioning input. This work
presents the first exploration of the potential of class conditional diffusion
models for 2D medical image classification. First, we develop a novel majority
voting scheme shown to improve the performance of medical diffusion
classifiers. Next, extensive experiments on the CheXpert and ISIC Melanoma skin
cancer datasets demonstrate that foundation and trained-from-scratch diffusion
models achieve competitive performance against SOTA discriminative classifiers
without the need for explicit supervision. In addition, we show that diffusion
classifiers are intrinsically explainable, and can be used to quantify the
uncertainty of their predictions, increasing their trustworthiness and
reliability in safety-critical, clinical contexts. Further information is
available on our project page:
https://faverogian.github.io/med-diffusion-classifier.github.io/
