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: Lecture Notes in Computer Science, cartea 10008
Editat de 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 Cornebiseen Limba Engleză Paperback – 27 sep 2016
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.
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Specificații
ISBN-13: 9783319469751
ISBN-10: 3319469754
Pagini: 280
Ilustrații: XIII, 280 p. 115 illus.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.42 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics
Locul publicării:Cham, Switzerland
ISBN-10: 3319469754
Pagini: 280
Ilustrații: XIII, 280 p. 115 illus.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.42 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics
Locul publicării:Cham, Switzerland
Cuprins
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.
Caracteristici
Includes supplementary material: sn.pub/extras