# Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary

arXiv: https://arxiv.org/abs/2111.10376 · HTML: https://mlsanity.com/paper/2111.10376/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques](https://mlsanity.com/paper/1908.05317/) (2019)
- [Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation](https://mlsanity.com/paper/2010.03341/) (2020)
- [Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation](https://mlsanity.com/paper/1708.01928/) (2017)
- [DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/1711.10448/) (2017)
- [Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers](https://mlsanity.com/paper/2104.03068/) (2021)
- [A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers](https://mlsanity.com/paper/2105.07763/) (2021)

## Built on by (most cited first, 8 of 8)

- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation](https://mlsanity.com/paper/2204.11618/) (2022)
- [Development of Diabetic Foot Ulcer Datasets: An Overview](https://mlsanity.com/paper/2201.00163/) (2022)
- [Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers](https://mlsanity.com/paper/2112.00065/) (2021)
- [Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2304.12987/) (2023)
- [Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement](https://mlsanity.com/paper/2305.01044/) (2023)
- [AutoMiSeg: Automatic Medical Image Segmentation via Test-Time Adaptation of Foundation Models](https://mlsanity.com/paper/2505.17931/) (2025)
- [Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring](https://mlsanity.com/paper/2607.05625/) (2026)

## Abstract (arXiv)

Diabetic foot ulcer classification systems use the presence of wound infection (bacteria present within the wound) and ischaemia (restricted blood supply) as vital clinical indicators for treatment and prediction of wound healing. Studies investigating the use of automated computerised methods of classifying infection and ischaemia within diabetic foot wounds are limited due to a paucity of publicly available datasets and severe data imbalance in those few that exist. The Diabetic Foot Ulcer Challenge 2021 provided participants with a more substantial dataset comprising a total of 15,683 diabetic foot ulcer patches, with 5,955 used for training, 5,734 used for testing and an additional 3,994 unlabelled patches to promote the development of semi-supervised and weakly-supervised deep learning techniques. This paper provides an evaluation of the methods used in the Diabetic Foot Ulcer Challenge 2021, and summarises the results obtained from each network. The best performing network was an ensemble of the results of the top 3 models, with a macro-average F1-score of 0.6307.