Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers
<|reference_start|>Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers: 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.<|reference_end|>
@article{busaranuvong2024guided,
    title={Guided Conditional Diffusion Classifier (ConDiff) for Enhanced
  Prediction of Infection in Diabetic Foot Ulcers},
    author={Palawat Busaranuvong, Emmanuel Agu, Deepak Kumar, Shefalika Gautam,
  Reza Saadati Fard, Bengisu Tulu, Diane Strong},
    journal={arXiv preprint arXiv:2405.00858},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00858},
    primaryClass={cs.CV}
}