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Diabetic Foot Ulcers Grand Challenge: Second Challenge, DFUC 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 1st ed. 2022 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 121 pages, aukštis x plotis: 235x155 mm, weight: 215 g, 47 Illustrations, color; 5 Illustrations, black and white; IX, 121 p. 52 illus., 47 illus. in color., 1 Paperback / softback
  • Serija: Image Processing, Computer Vision, Pattern Recognition, and Graphics 13183
  • Išleidimo metai: 21-Jan-2022
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030949060
  • ISBN-13: 9783030949068
Kitos knygos pagal šią temą:
Diabetic Foot Ulcers Grand Challenge: Second Challenge, DFUC 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 1st ed. 2022Didesnis paveikslėlis
  • Formatas: Paperback / softback, 121 pages, aukštis x plotis: 235x155 mm, weight: 215 g, 47 Illustrations, color; 5 Illustrations, black and white; IX, 121 p. 52 illus., 47 illus. in color., 1 Paperback / softback
  • Serija: Image Processing, Computer Vision, Pattern Recognition, and Graphics 13183
  • Išleidimo metai: 21-Jan-2022
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030949060
  • ISBN-13: 9783030949068
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783030949068.html
Raktažodžiai:

This book constitutes the Second Diabetic Foot Ulcers Grand Challenge, DFUC 2021, which was held on September 27, 2021, in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually due to the COVID-19 pandemic.

The 6 full papers included in this book were carefully reviewed and selected from 14 submissions. There is also an overview paper on the challenge and datasets and one summary paper of DFUC 2021. 


Development of Diabetic Foot Ulcer Datasets: An Overview.- DFUC2021
Challenge Papers.- Convolutional Nets Versus Vision Transformers for Diabetic
Foot Ulcer Classification.- Boosting EffcientNets Ensemble Performance via
Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia
Classification in Diabetic Foot Ulcers.- Bias Adjustable Activation Network
for Imbalanced data Diabetic Foot Ulcer Challenge 2021.- Effcient
Multi-model Vision Transformer based on Feature Fusion for Classification of
DFUC2021 Challenge.- Diabetic Foot Ulcer Classification using Well-known Deep
Learning Architectures.- Diabetic Foot Ulcer Grand Challenge 2021: Evaluation
and Summary.- Post Challenge Paper.- Deep Subspace analysing for
Semi-Supervised multi-label classification of Diabetic Foot Ulcer.