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Diabetic foot ulcers are a common manifestation of lesions on the diabeticfoot, a syndrome acquired as a long-term complication of diabetes mellitus.Accompanying neuropathy and vascular damage promote acquisition of pressureinjuries and tissue death due to ischaemia. Affected areas are prone toinfections, hindering the healing progress. The research at hand investigatesan approach on classification of infection and ischaemia, conducted as part ofthe Diabetic Foot Ulcer Challenge (DFUC) 2021. Different models of theEfficientNet family are utilized in ensembles. An extension strategy for thetraining data is applied, involving pseudo-labeling for unlabeled images, andextensive generation of synthetic images via pix2pixHD to cope with severeclass imbalances. The resulting extended training dataset features $8.68$ timesthe 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 extendedtraining dataset are compared. Synthetic images featured a broad qualitativevariety. Results show that models trained on the extended training dataset aswell as their ensemble benefit from the large extension. F1-Scores for rareclasses receive outstanding boosts, while those for common classes are eithernot harmed or boosted moderately. A critical discussion concretizes benefitsand identifies limitations, suggesting improvements. The work concludes thatclassification performance of individual models as well as that of ensemblescan be boosted utilizing synthetic images. Especially performance for rareclasses benefits notably.

lecture notes in computer science

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