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Deep Learning and Data Labeling for Medical Applications : First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings / edited by Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 10008
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 10008
- Language:
- English
- Subjects (All):
- Computer vision.
- Pattern recognition systems.
- Artificial intelligence.
- Computer graphics.
- Medical informatics.
- Computer Vision.
- Automated Pattern Recognition.
- Artificial Intelligence.
- Computer Graphics.
- Health Informatics.
- Local Subjects:
- Computer Vision.
- Automated Pattern Recognition.
- Artificial Intelligence.
- Computer Graphics.
- Health Informatics.
- Physical Description:
- 1 online resource (XIII, 280 pages) : 115 illustrations
- Edition:
- 1st ed. 2016.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2016.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty. The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
- Contents:
- Active learning
- Semi-supervised learning
- Reinforcement learning
- Domain adaptation and transfer learning
- Crowd-sourcing annotations and fusion of labels from different sources
- Data augmentation
- Modelling of label uncertainty
- Visualization and human-computer interaction
- Image description
- Medical imaging-based diagnosis
- Medical signal-based diagnosis
- Medical image reconstruction and model selection using deep learning techniques
- Meta-heuristic techniques for fine-tuning
- Parameter in deep learning-based architectures
- Applications based on deep learning techniques.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-46976-8
- 9783319469768
- Access Restriction:
- Restricted for use by site license.
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