Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology [electronic resource] : 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 / edited by Seyed Mostafa Kia, Hassan Mohy-ud-Din, Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Jane Maryam Rondina, Chantal Tax, Hongzhi Wang, Thomas Wolfers, Saima Rathore, Madhura Ingalhalikar.
Contributor(s): Kia, Seyed Mostafa [editor.]
| Mohy-ud-Din, Hassan [editor.] | Abdulkadir, Ahmed [editor.] | Bass, Cher [editor.] | Habes, Mohamad [editor.] | Rondina, Jane Maryam [editor.] | Tax, Chantal [editor.] | Wang, Hongzhi [editor.] | Wolfers, Thomas [editor.] | Rathore, Saima [editor.] | Ingalhalikar, Madhura [editor.] | SpringerLink (Online service).
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BookSeries: Image Processing, Computer Vision, Pattern Recognition, and Graphics: 12449Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XVIII, 305 p. 8 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030668433.Subject(s): Computer vision | Computer VisionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online
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Springer Nature eBookSummary: 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.
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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