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020 _a9783319469768
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245 1 0 _aDeep Learning and Data Labeling for Medical Applications
_h[electronic resource] :
_bFirst International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings /
_cedited 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.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIII, 280 p. 115 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
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338 _aonline resource
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v10008
505 0 _aActive 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.
520 _aThis 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.
650 0 _aComputer vision.
_9172122
650 0 _aPattern recognition systems.
_93953
650 0 _aArtificial intelligence.
_93407
650 0 _aComputer graphics.
_94088
650 0 _aMedical informatics.
_94729
650 1 4 _aComputer Vision.
_9172123
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Graphics.
_94088
650 2 4 _aHealth Informatics.
_931799
700 1 _aCarneiro, Gustavo.
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700 1 _aMateus, Diana.
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700 1 _aPeter, Loïc.
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700 1 _aBradley, Andrew.
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700 1 _aTavares, João Manuel R. S.
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700 1 _aBelagiannis, Vasileios.
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700 1 _aPapa, João Paulo.
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700 1 _aNascimento, Jacinto C.
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700 1 _aLoog, Marco.
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700 1 _aLu, Zhi.
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700 1 _aCardoso, Jaime S.
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700 1 _aCornebise, Julien.
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830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
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