東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Machine learning for medical image r...

MLMIR (Workshop) (2020 :)

Linked to FindBook

Google Book

Amazon

博客來

Machine learning for medical image reconstruction = third International Workshop, MLMIR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for medical image reconstruction/ edited by Farah Deeba ... [et al.].
Reminder of title:	third International Workshop, MLMIR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /
remainder title:	MLMIR 2020
other author:	Deeba, Farah.
corporate name:	MLMIR (Workshop)
Published:	Cham :Springer International Publishing : : 2020.,
Description:	viii, 163 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Diagnostic imaging - Congresses. - Data processing -
Online resource:	https://doi.org/10.1007/978-3-030-61598-7
ISBN:	9783030615987

Machine learning for medical image reconstruction = third International Workshop, MLMIR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /
Machine learning for medical image reconstructionthird International Workshop, MLMIR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /[electronic resource] :MLMIR 2020edited by Farah Deeba ... [et al.]. - Cham :Springer International Publishing :2020. - viii, 163 p. :ill., digital ;24 cm. - Lecture notes in computer science,124500302-9743 ;. - Lecture notes in computer science ;12450..

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.

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.

ISBN: 9783030615987

Standard No.: 10.1007/978-3-030-61598-7doiSubjects--Topical Terms:

893542
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / M595 2020

Dewey Class. No.: 006.31

Machine learning for medical image reconstruction = third International Workshop, MLMIR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings /
LDR:03165nmm a2200373 a 4500 001 2256412
003 DE-He213
005 20201019224543.0
006 m d
007 cr nn 008maaau
008 220420s2020 sz s 0 eng d
020 $a 9783030615987 $q (electronic bk.)
020 $a 9783030615970 $q (paper)
024 7 $a 10.1007/978-3-030-61598-7 $2 doi
035 $a 978-3-030-61598-7
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53 $b M595 2020
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a RC78.7.D53 $b M685 2020
111 2 $a MLMIR (Workshop) $n (3rd : $d 2020 : $c Online) $3 3526642
245 1 0 $a Machine learning for medical image reconstruction $h [electronic resource] : $b third International Workshop, MLMIR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 : proceedings / $c edited by Farah Deeba ... [et al.].
246 3 $a MLMIR 2020
246 3 $a MICCAI 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a viii, 163 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12450
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a 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.
520 $a 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.
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 893542
650 0 $a Artificial intelligence $x Medical applications $x Congresses. $3 660945
650 0 $a Machine learning $x Congresses. $3 576368
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 892702
650 2 4 $a Computers and Education. $3 892757
650 2 4 $a Computational Biology/Bioinformatics. $3 898313
700 1 $a Deeba, Farah. $3 3526643
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (23rd : $d 2020 : $c Online) $3 3492452
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 12450. $3 3526644
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 3382509
856 4 0 $u https://doi.org/10.1007/978-3-030-61598-7
950 $a Computer Science (SpringerNature-11645)