Buch, Englisch, 125 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 219 g
4th Challenge, DFUC 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings
Buch, Englisch, 125 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 219 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-80870-8
Verlag: Springer Nature Switzerland
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik E-Learning, Bildungstechnologie
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
Weitere Infos & Material
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.
