Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology: Third International Workshop, Mlcn 2020, and Second International Workshop, Rno-ai 2020, Held in Conjunction With Miccai 2020, Lima, Peru, October 4 8

Kia, Seyed Mostafa (Editor)/ Mohy-ud-din, Hassan (Editor)/ Abdulkadir, Ahmed (Editor)/ Bass, Cher (Editor)/ Habes, Mohamad (Editor)

ISBN 10: 3030668428 / ISBN 13: 9783030668426

Edité par Springer-Nature New York Inc, 2021

A propos de cet article

324 pages. 9.25x6.10x0.83 inches. In Stock.

N° de réf. du vendeur x-3030668428

Détails bibliographiques

• Titre: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology: Third International Workshop, Mlcn 2020, and Second International Workshop, Rno-ai 2020, Held in Conjunction With Miccai 2020, Lima, Peru, October 4 8
• Éditeur: Springer-Nature New York Inc
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 3030668428
• ISBN à 13 chiffres: 9783030668426
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.*

For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging.

For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience.

*The workshops were held virtually due to the COVID-19 pandemic.