Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (1st ed. 2021) (Lecture Notes in Computer Science #13003)

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 rigorous study of medical data related to machine learning systems.

Copyright:

2021

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9783030882105

Related ISBNs:

9783030882099

Publisher:

Springer International Publishing

Date of Addition:

09/29/21

Copyrighted By:

Springer Nature Switzerland AG

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Computers and Internet

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Sandy Engelhardt

Edited by:

Ilkay Oksuz

Edited by:

Dajiang Zhu

Edited by:

Yixuan Yuan

Edited by:

Anirban Mukhopadhyay

Edited by:

Nicholas Heller

Edited by:

Sharon Xiaolei Huang

Edited by:

Hien Nguyen

Edited by:

Raphael Sznitman

Edited by:

Yuan Xue