Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Dajiang Zhu

ISBN 10: 3030882098 / ISBN 13: 9783030882099

Edité par Springer Nature Switzerland AG, CH, 2021

A propos de cet article

This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021,  and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorousstudy of medical data related to machine learning systems.

N° de réf. du vendeur LU-9783030882099

Détails bibliographiques

• Titre: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections
• Éditeur: Springer Nature Switzerland AG, CH
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 3030882098
• ISBN à 13 chiffres: 9783030882099
• Reliure: Paperback
• Édition: 1st ed. 2021.
• État de l'article: New
• Dimensions: 15,5 L x 1,7 H x 23,5 P (centimètres) 15,5 centimètres de largeur par 1,7 centimètres de hauteur par 23,5 centimètres de profondeur
• Poids: 456 grammes

A propos de ce titre

Synopsis

This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.
DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.
For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorousstudy of medical data related to machine learning systems.