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020 _a9783319469768
_9978-3-319-46976-8
024 7 _a10.1007/978-3-319-46976-8
_2doi
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
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082 0 4 _a006.6
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082 0 4 _a006.37
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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, Lo�ic Peter, Andrew Bradley, Jo�ao Manuel R. S. Tavares, Vasileios Belagiannis, Jo�ao Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIII, 280 p. 115 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_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 science.
650 0 _aHealth informatics.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aImage processing.
650 0 _aPattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aPattern Recognition.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputer Graphics.
650 2 4 _aHealth Informatics.
700 1 _aCarneiro, Gustavo.
_eeditor.
700 1 _aMateus, Diana.
_eeditor.
700 1 _aPeter, Lo�ic.
_eeditor.
700 1 _aBradley, Andrew.
_eeditor.
700 1 _aTavares, Jo�ao Manuel R. S.
_eeditor.
700 1 _aBelagiannis, Vasileios.
_eeditor.
700 1 _aPapa, Jo�ao Paulo.
_eeditor.
700 1 _aNascimento, Jacinto C.
_eeditor.
700 1 _aLoog, Marco.
_eeditor.
700 1 _aLu, Zhi.
_eeditor.
700 1 _aCardoso, Jaime S.
_eeditor.
700 1 _aCornebise, Julien.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319469751
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v10008
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-46976-8
912 _aZDB-2-SCS
912 _aZDB-2-LNC
942 _cEBK
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