Buch, Englisch, 105 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1883 g
Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics
First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers
Buch, Englisch, 105 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1883 g
Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics
ISBN: 978-3-319-27928-2
Verlag: Springer International Publishing
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This book constitutes the revised selected papers of the
First International Workshop on Machine Learning in Medical Imaging, MLMMI
2015, held in July 2015 in Lille, France, in conjunction with the 32nd
International Conference on Machine Learning, ICML 2015.
The 10 papers presented in this volume were carefully
reviewed and selected for inclusion in the book. The papers communicate the
specific needs and nuances of medical imaging to the machine learning community
while exposing the medical imaging community to current trends in machine
learning.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
Weitere Infos & Material
Retrospective motion correction of magnitude-input MR images.- Automatic Brain Localization in Fetal MRI Using Superpixel Graphs.- Learning Deep Temporal Representations for fMRI Brain Decoding.- Modelling Non-Stationary and Non-Separable Spatio-Temporal Changes in Neurodegeneration via Gaussian Process Convolution.- Improving MRI brain image classification with anatomical regional kernels.- A Graph Based Classification Method for Multiple Sclerosis Clinical Form Using Support Vector Machine.- Classification of Alzheimer’s Disease using Discriminant Manifolds of Hippocampus Shapes.- Transfer Learning for Prostate Cancer Mapping Based on Multicentric MR imaging databases.
