TY  - BOOK
AU  - Yap,Moi Hoon
AU  - Kendrick,Connah
AU  - Cassidy,Bill
ED  - SpringerLink (Online service)
TI  - Diabetic Foot Ulcers Grand Challenge: Third Challenge, DFUC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 
T2  - Lecture Notes in Computer Science,
SN  - 9783031263545
AV  - TA1634 
U1  - 006.37 23
PY  - 2023///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Computer vision
KW  - Image processing
KW  - Machine learning
KW  - Social sciences
KW  - Data processing
KW  - Education
KW  - Software engineering
KW  - Computer Vision
KW  - Image Processing
KW  - Machine Learning
KW  - Computer Application in Social and Behavioral Sciences
KW  - Computers and Education
KW  - Software Engineering
N1  - Acceso multiusuario; Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification -- DFUC2022 Challenge Papers -- HarDNet-DFUS: Enhancing Backbone and Decoder of HarDNet-MSEGfor Diabetic Foot Ulcer Image Segmentation -- OCRNet For Diabetic Foot Ulcer Segmentation Combined with Edge Loss 30 -- On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness -- Capture the Devil in the Details via Partition-then-Ensemble on Higher Resolution Images -- Unconditionally Generated and Pseudo-Labeled Synthetic Images for Diabetic Foot Ulcer Segmentation Dataset Extension.-Post Challenge Paper -- Diabetic Foot Ulcer Segmentation Using Convolutional and Transformer-based Refined Mixup Augmentation for Diabetic Foot Ulcer Segmentation -- Organization IX DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framework with Vision Transformer Based Detection -- Summary Paper -- Diabetic Foot Ulcer Grand Challenge 2022 Summary
N2  - This book constitutes the Third Diabetic Foot Ulcers Grand Challenge, DFUC 2022, which was held on September 2022, in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 in Singapore. The 8 full papers presented together with 5 challenge papers and 3 post-challenge papers included in this book were carefully reviewed and selected from 19 submissions. The DFU challenges aim to motivate the health care domain to share datasets, participate in ground truth annotation, and enable data-innovation in computer algorithm development. In the longer term, it will lead to improved patient care
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-26354-5
ER  -