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.

使用 Guided Conditional Diffusion Classifier（ConDiff）来检测糖尿病足溃疡（DFUs）中的感染性伤口，预防严重并发症和截肢。

引导式条件扩散分类器（ConDiff）用于增强糖尿病足溃疡感染预测

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

Diabetic foot ulcers pose health risks, including higher morbidity,
mortality, and amputation rates. Monitoring wound areas is crucial for proper
care, but manual segmentation is subjective due to complex wound features and
background variation. Expert annotations are costly and time-intensive, thus
hampering large dataset creation. Existing segmentation models relying on
extensive annotations are impractical in real-world scenarios with limited
annotated data. In this paper, we propose a cross-domain augmentation method
named TransMix that combines Augmented Global Pre-training AGP and Localized
CutMix Fine-tuning LCF to enrich wound segmentation data for model learning.
TransMix can effectively improve the foot ulcer segmentation model training by
leveraging other dermatology datasets not on ulcer skins or wounds. AGP
effectively increases the overall image variability, while LCF increases the
diversity of wound regions. Experimental results show that TransMix increases
the variability of wound regions and substantially improves the Dice score for
models trained with only 40 annotated images under various proportions.

提出了一种名为 TransMix 的跨域增强方法，通过结合 AGP 和 LCF 来丰富足溃疡分割数据，从而有效改善模型训练；实验证明，TransMix 可以提高足溃疡区域的变异性并显著提高只用 40 个注释图像训练的模型的 Dice 评分。

改善有限监督下的足部溃疡分割，利用跨域增强技术

Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain  Augmentation

We introduce AiD Regen, a novel system that generates 3D wound models
combining 2D semantic segmentation with 3D reconstruction so that they can be
printed via 3D bio-printers during the surgery to treat diabetic foot ulcers
(DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D
image capturing, semantic segmentation, boundary-guided point-cloud processing,
3D model reconstruction, and 3D printable G-code generation, into a single
system that can be used out of the box. We developed a multi-stage data
preprocessing method to handle small and unbalanced DFU image datasets. AiD
Regen's human-in-the-loop machine learning interface enables clinicians to not
only create 3D regenerative patches with just a few touch interactions but also
customize and confirm wound boundaries. As evidenced by our experiments, our
model outperforms prior wound segmentation models and our reconstruction
algorithm is capable of generating 3D wound models with compelling accuracy. We
further conducted a case study on a real DFU patient and demonstrated the
effectiveness of AiD Regen in treating DFU wounds.

AiD Regen 使用 2D 语义分割和 3D 重建生成 3D 伤口模型，协助治疗糖尿病足溃疡。它具有全流程，包括图像捕捉，语义分割，点云处理，3D 重建和 G 代码生成的能力，并具有人机交互的接口。实验证明，该模型优于先前的伤口分割模型，并能够生成具有较高准确性的 3D 伤口模型。同时，对患有糖尿病足溃疡的真实病例进行了病例研究，并展示了 AiD Regen 在治疗 DFU 创伤方面的有效性。

使用创面分割和重建生成 3D 生物可打印补片以治疗糖尿病足溃疡

Generating 3D Bio-Printable Patches Using Wound Segmentation and  Reconstruction to Treat Diabetic Foot Ulcers

There has been a substantial amount of research involving computer methods
and technology for the detection and recognition of diabetic foot ulcers
(DFUs), but there is a lack of systematic comparisons of state-of-the-art deep
learning object detection frameworks applied to this problem. DFUC2020 provided
participants with a comprehensive dataset consisting of 2,000 images for
training and 2,000 images for testing. This paper summarises the results of
DFUC2020 by comparing the deep learning-based algorithms proposed by the
winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble
method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For
each deep learning method, we provide a detailed description of model
architecture, parameter settings for training and additional stages including
pre-processing, data augmentation and post-processing. We provide a
comprehensive evaluation for each method. All the methods required a data
augmentation stage to increase the number of images available for training and
a post-processing stage to remove false positives. The best performance was
obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean
average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we
demonstrate that the ensemble method based on different deep learning methods
can enhanced the F1-Score but not the mAP.

本文总结了 DFUC2020 举办的比赛结果，比较了各获胜团队所提出的基于深度学习的算法，主要采用 Faster R-CNN 算法实现对糖尿病足溃疡的识别和检测。