Diabetic Foot Ulcers Grand Challenge - 4th Challenge, DFUC 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings

This book constitutes the 4th Challenge on Diabetic Foot Ulcers, DFUC2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024.
The 8 full papers presented in this book together with 2 invited papers were carefully reviewed and selected from 11 submissions.
The task of DFUC 2024 was on self-supervised learning in ulcer segmentation, for the purpose of supporting research towards more advanced methods to overcome data deficiency and unlabelled data.

Sommario

Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation.- Dinov2 Mask R-CNN: Self-supervised Instance Segmentation of Diabetic Foot Ulcers.- Diabetic foot ulcer unsupervised segmentation with Vision Transformers attention.- Self-Supervised Instance Segmentation of Diabetic Foot Ulcers via Feature Correspondence Distillation.- Multi-stage Segmentation of Diabetic Foot Ulcers Using Self-Supervised Learning.- SSL-based Encoder Pre-training for Segmenting a Heterogeneous Chronic Wound Image Database with Few Annotations.- Multi-Scale Attention Network for Diabetic Foot Ulcer Segmentation using Self-Supervised Learning.- A Supervised Segmentation Solution: Diabetic Foot Ulcers Challenge 2024.- CDe: Focus on the Color Differences in Diabetic Foot Images.- Diabetic Foot Ulcer Grand Challenge 2024: Overview and Baseline Methods.

Riassunto

This book constitutes the 4th Challenge on Diabetic Foot Ulcers, DFUC2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024.

The 8 full papers presented in this book together with 2 invited papers were carefully reviewed and selected from 11 submissions.

The task of DFUC 2024 was on self-supervised learning in ulcer segmentation, for the purpose of supporting research towards more advanced methods to overcome data deficiency and unlabelled data.

Dettagli sul prodotto

• Con la collaborazione di: Raphael Brüngel (Editore), Connah Kendrick (Editore), Moi Hoon Yap (Editore)
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 05.03.2025
• EAN: 9783031808708
• ISBN: 978-3-0-3180870-8
• Pagine: 125
• Dimensioni: 155 mm x 8 mm x 235 mm
• Peso: 219 g
• Illustrazioni: X, 125 p. 46 illus., 44 illus. in color.
• Serie: Lecture Notes in Computer Science
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Software applicativo; Elektronik, machine learning, Lehrmittel, Lerntechnologien, E-Learning, Maschinelles Lernen, angewandte informatik, Bildverarbeitung, Computer Vision, Computer Imaging, Vision, Pattern Recognition and Graphics, Digitale Signalverarbeitung (DSP), Computer and Information Systems Applications, Computers and Education, Image processing, Signal, Speech and Image Processing, Instance segmentation, self-supervised learning, synthetic images, diabetic foot ulcers