Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
305
Utgivningsdatum
2020-12-31
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Kia, Seyed Mostafa (ed.), Rathore, Saima (ed.), Rondina, Jane Maryam (ed.), Mohy-Ud-Din, Hassan (ed.), Abdulkadir, Ahmed (ed.), Bass, Cher (ed.), Habes, Mohamad (ed.), Ingalhalikar, Madhura (ed.), Tax, Chantal (ed.), Wang, Hongzhi (ed.), Wolfers, Thomas (ed.)
Illustrationer
8 Illustrations, black and white; XVIII, 305 p. 8 illus.
Dimensioner
234 x 156 x 17 mm
Vikt
454 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030668426
Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology (häftad)

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, 2020, Proceedings

Häftad Engelska, 2020-12-31
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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.
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