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024 7 _a10.1007/978-3-030-61598-7
_2doi
050 4 _aQ334-342
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072 7 _aUYQ
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072 7 _aCOM004000
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245 1 0 _aMachine Learning for Medical Image Reconstruction
_h[electronic resource] :
_bThird International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /
_cedited by Farah Deeba, Patricia Johnson, Tobias Würfl, Jong Chul Ye.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aVIII, 163 p. 76 illus., 48 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v12450
505 0 _aDeep Learning for Magnetic Resonance Imaging -- 3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI -- Deep Parallel MRI Reconstruction Network Without Coil Sensitivities -- Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data -- Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI -- Model-based Learning for Quantitative Susceptibility Mapping -- Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks -- Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping -- Data-Consistency in Latent Space and Online Update Strategy to Guide GAN for Fast MRI Reconstruction -- Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI -- AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesis -- Deep Learning for General Image Reconstruction -- A deep prior approach to magnetic particle imaging -- End-To-End Convolutional NeuralNetwork for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images -- Cellular/Vascular Reconstruction using a Deep CNN for Semantic Image Preprocessing and Explicit Segmentation -- Improving PET-CT Image Segmentation via Deep Multi-Modality Data Augmentation -- Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning.
520 _aThis book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
650 0 _aArtificial intelligence.
650 0 _aComputer vision.
650 0 _aSocial sciences
_xData processing.
650 0 _aEducation
_xData processing.
650 0 _aBioinformatics.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputer Vision.
650 2 4 _aComputer Application in Social and Behavioral Sciences.
650 2 4 _aComputers and Education.
650 2 4 _aComputational and Systems Biology.
700 1 _aDeeba, Farah.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aJohnson, Patricia.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWürfl, Tobias.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aYe, Jong Chul.
_eeditor.
_4edt
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030615970
776 0 8 _iPrinted edition:
_z9783030615994
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v12450
856 4 0 _uhttps://doi.org/10.1007/978-3-030-61598-7
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