Machine Learning for Medical Image Reconstruction (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
163
Utgivningsdatum
2020-10-20
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Deeba, Farah (ed.), Ye, Jong Chul (ed.), Würfl, Tobias (ed.), Johnson, Patricia (ed.)
Illustrationer
48 Illustrations, color; 28 Illustrations, black and white; VIII, 163 p. 76 illus., 48 illus. in col
Dimensioner
234 x 156 x 9 mm
Vikt
250 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030615970

Machine Learning for Medical Image Reconstruction

Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

av Farah Deeba, Patricia Johnson, Tobias Wrfl, Jong Chul Ye
Häftad,  Engelska, 2020-10-20
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This 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.
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Farah Deeba, Patricia Johnson, Tobias Wrfl, Jong Chul Ye

Innehållsförteckning

Deep 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.

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