Cantitate/Preț
Produs
Total produse
Vezi coșul
Machine Learning for Medical Image Reconstruction: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings de Nandinee Haq

Machine Learning for Medical Image Reconstruction: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings: Lecture Notes in Computer Science, cartea 12964

Editat de Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo
en Limba Engleză Paperback – 30 sep 2021
This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.  The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 34026 lei

Preț vechi: 42533 lei
-20% Nou

Puncte Express: 510
Preț estimativ în valută:
6022€ • 7038$ • 5228£

Carte tipărită la comandă

Livrare economică 17 februarie-03 martie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783030885519
ISBN-10: 3030885518
Pagini: 142
Ilustrații: VIII, 142 p. 53 illus., 37 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.22 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

Deep Learning for Magnetic Resonance Imaging.- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks.- Efficient Image Registration Network For Non-Rigid Cardiac Motion Estimation.- Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge.- Self-Supervised Dynamic MRI Reconstruction.- A Simulation Pipeline to Generate Realistic Breast Images For Learning DCE-MRI Reconstruction.- Deep MRI Reconstruction with Generative Vision Transformers.- Distortion Removal and Deblurring of Single-Shot DWI MRI Scans.- One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline.- Physics-informed self-supervised deep learning reconstruction for accelerated rst-pass perfusion cardiac MRI.- Deep Learning for General Image Reconstruction.- Noise2Stack: Improving Image Restoration by Learning from Volumetric Data.- Real-time Video Denoising in Fluoroscopic Imaging.- A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution.- Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks.