# Development of Diabetic Foot Ulcer Datasets: An Overview

arXiv: https://arxiv.org/abs/2201.00163 · HTML: https://mlsanity.com/paper/2201.00163/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 1 Jan 2022.

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

- [Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2010.05855/) (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)
- [Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary](https://mlsanity.com/paper/2111.10376/) (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, 3 of 3)

- [Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation](https://mlsanity.com/paper/2204.11618/) (2022)
- [Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2304.12987/) (2023)
- [Diabetic Foot Ulcer Grand Challenge 2022 Summary](https://mlsanity.com/paper/2304.12001/) (2023)

## Abstract (arXiv)

This paper provides conceptual foundation and procedures used in the development of diabetic foot ulcer datasets over the past decade, with a timeline to demonstrate progress. We conduct a survey on data capturing methods for foot photographs, an overview of research in developing private and public datasets, the related computer vision tasks (detection, segmentation and classification), the diabetic foot ulcer challenges and the future direction of the development of the datasets. We report the distribution of dataset users by country and year. Our aim is to share the technical challenges that we encountered together with good practices in dataset development, and provide motivation for other researchers to participate in data sharing in this domain.