Machine Learning for Medical Image Reconstruction : 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / edited by Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo.

Format:

Book

Contributor:

Haq, Nandinee, Editor.

Johnson, Patricia, Editor.

Maier, Andreas., Editor.

Würfl, Tobias, Editor.

Yoo, Jaejun., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12964

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12964

Language:

English

Subjects (All):

Artificial intelligence.

Artificial Intelligence.

Local Subjects:

Artificial Intelligence.

Physical Description:

1 online resource (VIII, 142 pages) : 53 illustrations, 37 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-88552-6

9783030885526

Access Restriction:

Restricted for use by site license.