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Machine learning for medical image reconstruction4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for medical image reconstructionedited by Nandinee Haq ... [et al.].
Reminder of title:	4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
remainder title:	MLMIR 2021
other author:	Haq, Nandinee.
corporate name:
Published:	Cham :Springer International Publishing :2021.
Description:	viii, 142 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Diagnostic imagingCongresses.Data processing
Online resource:	https://doi.org/10.1007/978-3-030-88552-6
ISBN:	9783030885526$q(electronic bk.)

Machine learning for medical image reconstruction4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
Machine learning for medical image reconstruction4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /[electronic resource] :MLMIR 2021edited by Nandinee Haq ... [et al.]. - Cham :Springer International Publishing :2021. - viii, 142 p. :ill., digital ;24 cm. - Lecture notes in computer science,129640302-9743 ;. - Lecture notes in computer science ;4891..

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.

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.

ISBN: 9783030885526$q(electronic bk.)

Standard No.: 10.1007/978-3-030-88552-6doiSubjects--Topical Terms:

445765
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / M595 2021

Dewey Class. No.: 006.31

Machine learning for medical image reconstruction4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
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520 $a 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.
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