Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers

TL;DR

This paper introduces ConDiff, a novel deep-learning model that combines guided image synthesis and diffusion processes to improve infection detection in diabetic foot ulcers from photographs, achieving higher accuracy than existing methods.

Contribution

The paper presents a new guided conditional diffusion classifier that integrates generative and discriminative techniques for medical image analysis, specifically for infection detection in DFUs.

Findings

ConDiff achieved 83% accuracy and 0.858 F1-score, surpassing state-of-the-art models.

The use of triplet loss reduces overfitting in the classification process.

The approach demonstrates the potential of generative discriminative models in medical diagnostics.

Abstract

To detect infected wounds in Diabetic Foot Ulcers (DFUs) from photographs, preventing severe complications and amputations. Methods: This paper proposes the Guided Conditional Diffusion Classifier (ConDiff), a novel deep-learning infection detection model that combines guided image synthesis with a denoising diffusion model and distance-based classification. The process involves (1) generating guided conditional synthetic images by injecting Gaussian noise to a guide image, followed by denoising the noise-perturbed image through a reverse diffusion process, conditioned on infection status and (2) classifying infections based on the minimum Euclidean distance between synthesized images and the original guide image in embedding space. Results: ConDiff demonstrated superior performance with an accuracy of 83% and an F1-score of 0.858, outperforming state-of-the-art models by at least 3%. The use of a triplet loss function reduces overfitting in the distance-based classifier. Conclusions: ConDiff not only enhances diagnostic accuracy for DFU infections but also pioneers the use of generative discriminative models for detailed medical image analysis, offering a promising approach for improving patient outcomes.