Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary
This addresses the limited availability of public datasets for diabetic foot ulcer classification, which is crucial for medical treatment and wound healing prediction, but it is incremental as it builds on existing challenge frameworks.
The paper tackled the problem of automated classification of infection and ischaemia in diabetic foot ulcers by evaluating methods from a challenge using a dataset of 15,683 patches, with the best model achieving a macro-average F1-score of 0.6307.
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.