Deep Learning and Data Labeling for Medical Applications, 1st ed. 2016 First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings Image Processing, Computer Vision, Pattern Recognition, and Graphics Series
Coordonnateurs : Carneiro Gustavo, Mateus Diana, Peter Loïc, Bradley Andrew, Tavares João Manuel R. S., Belagiannis Vasileios, Papa João Paulo, Nascimento Jacinto C., Loog Marco, Lu Zhi, Cardoso Jaime S., Cornebise Julien
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.
Includes supplementary material: sn.pub/extras
Date de parution : 09-2016
Ouvrage de 280 p.
15.5x23.5 cm
Thème de Deep Learning and Data Labeling for Medical Applications :
Mots-clés :
active learning; deep learning; human-computer interaction; label uncertainty; medical image analysis; anatomical structure segmentation; cell detection; clinical prediction; computer aided diagnosis; convolutional neural network; crowdsourcing; domain adaptation; MRI; multi-label annotation; neurosurgery; parameter approximation; semantic description; semi-supervised learning; transfer learning; machine learning