Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary

Cassidy, Bill; Kendrick, Connah; Reeves, Neil D.; Pappachan, Joseph M.; O'Shea, Claire; Armstrong, David G.; Yap, Moi Hoon

doi:10.48550/arxiv.2111.10376

Cited by 3 publications

(7 citation statements)

References 22 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A hough the proposed hybrid CNN architecture achieved promising results, it will be in esting to see how fine-tuning the network parameters can contribute to improving its p formance. In the MICCAI DFUC2021 challenge [55], a multiclass classification problem was troduced with image samples from the following classes: none, infection, ischemia, a both. The best scores were obtained by BiT-ResNeXt50 and EfficientNet-B3, which w trained on multiple data folds.…”

Section: Vardasca Et Al (2018)mentioning

confidence: 99%

See 1 more Smart Citation

Diabetic Foot Ulcer Identification: A Review

et al. 2023

View full text Add to dashboard Cite

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal.

show abstract

Section: Vardasca Et Al (2018)mentioning

confidence: 99%

“…In the MICCAI DFUC2021 challenge [ 55 ], a multiclass classification problem was introduced with image samples from the following classes: none, infection, ischemia, and both. The best scores were obtained by BiT-ResNeXt50 and EfficientNet-B3, which were trained on multiple data folds.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Foot Ulcer Identification: A Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The organisers continue to support the research community with a live leaderboard on the Grand Challenge System 10 . At the time of writing this paper, the best macro F1-score on the live leaderboard is 0.6307 [33].…”

Section: Dfu Challengesmentioning

confidence: 99%

Development of Diabetic Foot Ulcer Datasets: An Overview

Yap¹,

Kendrick²,

Reeves³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…This model outperformed the remaining models for the F1-score of the ischaemia class. More precise documentation about the challenge results are summarized in [4].…”

Section: Extended Model and Ensemble Performancesmentioning

confidence: 99%

“…Lancashire Teaching Hospitals: https://www.lancsteachinghospitals.nhs.uk/, access 2021-09-224 EfficientNet: https://github.com/mingxingtan/efficientnet, access 2021-10-03 5 pix2pixHD: https://github.com/NVIDIA/pix2pixHD, access 2021-09-12…”

mentioning

confidence: 99%

Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers

Bloch,

Brüngel,

Friedrich

2021

Preprint

View full text Add to dashboard Cite

Diabetic foot ulcers are a common manifestation of lesions on the diabetic foot, a syndrome acquired as a long-term complication of diabetes mellitus. Accompanying neuropathy and vascular damage promote acquisition of pressure injuries and tissue death due to ischaemia. Affected areas are prone to infections, hindering the healing progress. The research at hand investigates an approach on classification of infection and ischaemia, conducted as part of the Diabetic Foot Ulcer Challenge (DFUC) 2021. Different models of the EfficientNet family are utilized in ensembles. An extension strategy for the training data is applied, involving pseudo-labeling for unlabeled images, and extensive generation of synthetic images via pix2pixHD to cope with severe class imbalances. The resulting extended training dataset features 8.68 times the size of the baseline and shows a real to synthetic image ratio of 1 : 3. Performances of models and ensembles trained on the baseline and extended training dataset are compared. Synthetic images featured a broad qualitative variety. Results show that models trained on the extended training dataset as well as their ensemble benefit from the large extension. F1-Scores for rare classes receive outstanding boosts, while those for common classes are either not harmed or boosted moderately. A critical discussion concretizes benefits and identifies limitations, suggesting improvements. The work concludes that classification performance of individual models as well as that of ensembles can be boosted utilizing synthetic images. Especially performance for rare classes benefits notably.

show abstract

Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary

Cited by 3 publications

References 22 publications

Diabetic Foot Ulcer Identification: A Review

Diabetic Foot Ulcer Identification: A Review

Development of Diabetic Foot Ulcer Datasets: An Overview

Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers

Contact Info

Product

Resources

About