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Machine Learning for Medical Image Reconstruction Farah Deeba

Machine Learning for Medical Image Reconstruction von Farah Deeba

Machine Learning for Medical Image Reconstruction Farah Deeba

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Zusammenfassung

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.

Machine Learning for Medical Image Reconstruction Zusammenfassung

Machine Learning for Medical Image Reconstruction: Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings Farah Deeba

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.

Inhaltsverzeichnis

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 Neural Network 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.

Zusätzliche Informationen

GOR012968596
9783030615970
3030615979
Machine Learning for Medical Image Reconstruction: Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings Farah Deeba
Farah Deeba
Image Processing, Computer Vision, Pattern Recognition, and Graphics
Gebraucht - Sehr Gut
Broschiert
Springer Nature Switzerland AG
20201020
163
N/A
Die Abbildung des Buches dient nur Illustrationszwecken, die tatsächliche Bindung, das Cover und die Auflage können sich davon unterscheiden.
Dies ist ein gebrauchtes Buch. Es wurde schon einmal gelesen und weist von der früheren Nutzung Gebrauchsspuren auf. Wir gehen davon aus, dass es im Großen und Ganzen in einem sehr guten Zustand ist. Sollten Sie jedoch nicht vollständig zufrieden sein, setzen Sie sich bitte mit uns in Verbindung.