TY - GEN
T1 - Diabetic Foot Ulcer Grand Challenge 2021
T2 - 2nd Diabetic Foot Ulcers Grand Challenge, DFUC 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
AU - Cassidy, Bill
AU - Kendrick, Connah
AU - Reeves, Neil D.
AU - Pappachan, Joseph M.
AU - O’Shea, Claire
AU - Armstrong, David G.
AU - Yap, Moi Hoon
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-030-94907-5_7
DO - 10.1007/978-3-030-94907-5_7
M3 - Conference contribution
AN - SCOPUS:85124153024
SN - 9783030949068
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 90
EP - 105
BT - Diabetic Foot Ulcers Grand Challenge - 2nd Challenge, DFUC 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Yap, Moi Hoon
A2 - Cassidy, Bill
A2 - Kendrick, Connah
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 September 2021 through 27 September 2021
ER -