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.
Abstract (translated)
为了从照片中检测糖尿病足溃疡（DFUs）中的感染伤口，预防和减轻严重并发症和截肢，本文提出了一种名为引导条件扩散分类器（ConDiff）的新颖深度学习感染检测模型，它结合了指导图像生成和去噪扩散模型以及距离分类。该过程包括：（1）通过向指导图像注入高斯噪声来生成指导条件合成图像，然后通过反扩散过程对噪声污染的图像进行去噪，条件是感染状况；（2）根据合成图像与嵌入空间中原始指导图像之间的最小欧氏距离，对感染进行分类。结果：ConDiff在准确性和F1分数方面都表现出优异性能，至少超过了最先进的模型的3%。距离分类器的三元组损失函数有助于减少过拟合。结论：ConDiff不仅可以提高糖尿病足溃疡感染的诊断准确性，还开创了使用生成判别模型进行详细医学图像分析的先河，为提高患者治疗效果提供了有益的途径。
URL
https://arxiv.org/abs/2405.00858
