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Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Chall

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Specificaties

Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
ISBN-13
9783030681067
Book Title
Statistical Atlases and Computational Models of the Heart. M&Ms a
ISBN
9783030681067
Series
Lecture Notes in Computer Science Ser.
Publication Year
2021
Type
Textbook
Format
Trade Paperback
Language
English
Publication Name
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers
Author
Mihaela Pop
Item Length
9.3in
Publisher
Springer International Publishing A&G
Item Width
6.1in
Item Weight
23.4 Oz
Number of Pages
Xv, 417 Pages

Over dit product

Product Information

Regular papers .- A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI.- Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view.- Graph convolutional regression of cardiac depolarization from sparse endocardial maps.- A cartesian grid representation of left atrial appendages for deep learning based estimation of thrombogenic risk predictors.- Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks.- Modelling Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions- Towards mesh-free patient-specific mitral valve modeling.- PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps.- Automatic Detection of Landmarks for Fast Cardiac MR Image Registration.- Quality-aware semi-supervised learning for CMR segmentation.- Estimation of imaging biomarker's progression in post-infarct patients using cross-sectional data.- PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data.- Shape constrained CNN for cardiac MR segmentation with simultaneous prediction of shape and pose parameters.- Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI.- Estimation of Cardiac Valve Annuli Motion with Deep Learning.- 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration.- Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning.- M&Ms challenge .- Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation.- Disentangled Representations for Domain-generalized Cardiac Segmentation.- A 2-step Deep Learning method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation.- Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information.- Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer.- Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation.- Studying Robustness of Segmantic Segmentation under Domain Shift in cardiac MRI.- A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences.- Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-Gate UNET.- Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation.- Deidentifying MRI data domain by iterative backpropagation.- A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net.- Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation.- Style-invariant Cardiac Image Segmentation with Test-time Augmentation.- EMIDEC challenge .- Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI.- Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI.- Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information.- SM2N2: A Stacked Architecture for Multimodal Data and its Application to Myocardial Infarction Detection.- A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI.- Efficient 3D deep learning for myocardial diseases segmentation.- Deep-learning-based myocardial pathology detection.- Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks.- Uncertainty-based Segmentation of Myocardial Infarction Areas on Cardiac MR images.- Anatomy Prior Based U-net for Pathology Segmentation with Attention.- Au

Product Identifiers

Publisher
Springer International Publishing A&G
ISBN-10
3030681068
ISBN-13
9783030681067
eBay Product ID (ePID)
26050400150

Product Key Features

Author
Mihaela Pop
Publication Name
Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers
Format
Trade Paperback
Language
English
Series
Lecture Notes in Computer Science Ser.
Publication Year
2021
Type
Textbook
Number of Pages
Xv, 417 Pages

Dimensions

Item Length
9.3in
Item Width
6.1in
Item Weight
23.4 Oz

Additional Product Features

Series Volume Number
12592
Number of Volumes
1 Vol.
Lc Classification Number
Ta1634
Copyright Date
2021
Topic
Probability & Statistics / General, Intelligence (Ai) & Semantics, Computer Vision & Pattern Recognition
Illustrated
Yes
Genre
Computers, Mathematics

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Ik verklaar dat al mijn verkoopactiviteiten zullen voldoen aan alle wet- en regelgeving van de EU.
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