語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Information Processing in Medical Im...
~
Bao, Siqi.
Information Processing in Medical Imaging = 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Information Processing in Medical Imaging/ edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao.
其他題名:
26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings /
其他作者:
Chung, Albert C. S.
面頁冊數:
XIX, 884 p. 517 illus., 331 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Optical data processing. -
電子資源:
https://doi.org/10.1007/978-3-030-20351-1
ISBN:
9783030203511
Information Processing in Medical Imaging = 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings /
Information Processing in Medical Imaging
26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings /[electronic resource] :edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao. - 1st ed. 2019. - XIX, 884 p. 517 illus., 331 illus. in color.online resource. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11492. - Image Processing, Computer Vision, Pattern Recognition, and Graphics ;9219.
Segmentation -- A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration -- Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology -- Semi-Supervised and Task-Driven Data Augmentation -- Classification and Inference -- Analyzing Brain Morphology on the Bag-of-Features Manifold -- Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks -- Deep Learning -- InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction -- Adaptive Graph Convolution Pooling for Brain Surface Analysis -- On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging -- A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging -- Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation -- Reconstruction -- Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation -- Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences -- Disease Modeling -- Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia -- Shape -- Minimizing Non-Holonomicity: Finding Sheets in Fibrous Structures -- Learning Low-Dimensional Representations of Shape Data Sets with Diffeomorphic Autoencoders -- Diffeomorphic Medial Modeling -- Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing -- Registration -- Local Optimal Transport for Functional Brain Template Estimation -- Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations -- Learning Motion -- Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting -- Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces -- Functional Imaging -- Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG -- A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation -- White Matter Imaging -- Asymmetry Spectrum Imaging for Baby Diffusion Tractography -- A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis -- Posters -- 3D Organ Shape Reconstruction from Topogram Images -- A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation -- A Graph Model of the Lungs with MorphologyBased Structure for Tuberculosis Type Classification -- A Longitudinal Model for Tau Aggregation in Alzheimers Disease Based on Structural Connectivity -- Accurate Nuclear Segmentation with Center Vector Encoding -- Bayesian Longitudinal Modeling of Early Stage Parkinsons Disease Using DaTscan Images -- Brain Tumor Segmentation on MRI with Missing Modalities -- Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation -- DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction -- ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data -- FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms -- Graph Convolutional Nets for Tool Presence Detection in Surgical Videos -- High-Order Oriented Cylindrical Flux for Curvilinear Structure Detection and Vessel Segmentation -- Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network -- Learning a Conditional Generative Model for Anatomical Shape Analysis -- Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness -- Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data -- Riemannian Geometry Learning for Disease Progression Modelling -- Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model -- Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices with Applications to Neuroimaging -- Simultaneous Spatial-temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders -- Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention -- A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces -- A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data -- A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data -- A Model for Elastic Evolution on Foliated Shapes -- Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning -- New Graph-Blind Convolutional Network for Brain Connectome Data Analysis -- CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation -- Data-Driven Model Order Reduction For Diffeomorphic Image Registration -- DGR-Net: Deep Groupwise Registration of Multispectral Images -- Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery -- Generalizations of Ripleys K-Function with Application to Space Curves -- Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates -- InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-Contrast Microstructural MRI -- Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases -- Learning-Based Optimization of the Under-Sampling Pattern in MRI -- Melanoma Recognition via Visual Attention -- Nonlinear Markov Random Fields Learned via Backpropagation -- Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler -- SHAMANN: Shared Memory Augmented Neural Networks -- Signet Ring Cell Detection With a Semi-supervised Learning Framework -- Spherical U-Net on Cortical Surfaces: Methods and Applications -- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis.
This book constitutes the proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, held at the Hong Kong University of Science and Technology, Hong Kong, China, in June 2019. The 69 full papers presented in this volume were carefully reviewed and selected from 229 submissions. They were organized in topical sections on deep learning and segmentation; classification and inference; reconstruction; disease modeling; shape, registration; learning motion; functional imaging; and white matter imaging. The book also includes a number of post papers. .
ISBN: 9783030203511
Standard No.: 10.1007/978-3-030-20351-1doiSubjects--Topical Terms:
639187
Optical data processing.
LC Class. No.: TA1630-1650
Dewey Class. No.: 006.6
Information Processing in Medical Imaging = 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings /
LDR
:08390nam a22004335i 4500
001
1011621
003
DE-He213
005
20200703144946.0
007
cr nn 008mamaa
008
210106s2019 gw | s |||| 0|eng d
020
$a
9783030203511
$9
978-3-030-20351-1
024
7
$a
10.1007/978-3-030-20351-1
$2
doi
035
$a
978-3-030-20351-1
050
4
$a
TA1630-1650
072
7
$a
UYT
$2
bicssc
072
7
$a
COM012000
$2
bisacsh
072
7
$a
UYT
$2
thema
072
7
$a
UYQV
$2
thema
082
0 4
$a
006.6
$2
23
082
0 4
$a
006.37
$2
23
245
1 0
$a
Information Processing in Medical Imaging
$h
[electronic resource] :
$b
26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings /
$c
edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao.
250
$a
1st ed. 2019.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
XIX, 884 p. 517 illus., 331 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
$v
11492
505
0
$a
Segmentation -- A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration -- Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology -- Semi-Supervised and Task-Driven Data Augmentation -- Classification and Inference -- Analyzing Brain Morphology on the Bag-of-Features Manifold -- Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks -- Deep Learning -- InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction -- Adaptive Graph Convolution Pooling for Brain Surface Analysis -- On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging -- A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging -- Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation -- Reconstruction -- Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation -- Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences -- Disease Modeling -- Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia -- Shape -- Minimizing Non-Holonomicity: Finding Sheets in Fibrous Structures -- Learning Low-Dimensional Representations of Shape Data Sets with Diffeomorphic Autoencoders -- Diffeomorphic Medial Modeling -- Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing -- Registration -- Local Optimal Transport for Functional Brain Template Estimation -- Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations -- Learning Motion -- Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting -- Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces -- Functional Imaging -- Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG -- A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation -- White Matter Imaging -- Asymmetry Spectrum Imaging for Baby Diffusion Tractography -- A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis -- Posters -- 3D Organ Shape Reconstruction from Topogram Images -- A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation -- A Graph Model of the Lungs with MorphologyBased Structure for Tuberculosis Type Classification -- A Longitudinal Model for Tau Aggregation in Alzheimers Disease Based on Structural Connectivity -- Accurate Nuclear Segmentation with Center Vector Encoding -- Bayesian Longitudinal Modeling of Early Stage Parkinsons Disease Using DaTscan Images -- Brain Tumor Segmentation on MRI with Missing Modalities -- Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation -- DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction -- ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data -- FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms -- Graph Convolutional Nets for Tool Presence Detection in Surgical Videos -- High-Order Oriented Cylindrical Flux for Curvilinear Structure Detection and Vessel Segmentation -- Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network -- Learning a Conditional Generative Model for Anatomical Shape Analysis -- Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness -- Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data -- Riemannian Geometry Learning for Disease Progression Modelling -- Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model -- Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices with Applications to Neuroimaging -- Simultaneous Spatial-temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders -- Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention -- A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces -- A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data -- A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data -- A Model for Elastic Evolution on Foliated Shapes -- Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning -- New Graph-Blind Convolutional Network for Brain Connectome Data Analysis -- CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation -- Data-Driven Model Order Reduction For Diffeomorphic Image Registration -- DGR-Net: Deep Groupwise Registration of Multispectral Images -- Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery -- Generalizations of Ripleys K-Function with Application to Space Curves -- Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates -- InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-Contrast Microstructural MRI -- Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases -- Learning-Based Optimization of the Under-Sampling Pattern in MRI -- Melanoma Recognition via Visual Attention -- Nonlinear Markov Random Fields Learned via Backpropagation -- Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler -- SHAMANN: Shared Memory Augmented Neural Networks -- Signet Ring Cell Detection With a Semi-supervised Learning Framework -- Spherical U-Net on Cortical Surfaces: Methods and Applications -- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis.
520
$a
This book constitutes the proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, held at the Hong Kong University of Science and Technology, Hong Kong, China, in June 2019. The 69 full papers presented in this volume were carefully reviewed and selected from 229 submissions. They were organized in topical sections on deep learning and segmentation; classification and inference; reconstruction; disease modeling; shape, registration; learning motion; functional imaging; and white matter imaging. The book also includes a number of post papers. .
650
0
$a
Optical data processing.
$3
639187
650
0
$a
Artificial intelligence.
$3
559380
650
0
$a
Computer science—Mathematics.
$3
1253519
650
0
$a
Health informatics.
$3
1064466
650
0
$a
Computers.
$3
565115
650
0
$2
lc
$a
Operating systems (Computers).
$3
868175
650
1 4
$a
Image Processing and Computer Vision.
$3
670819
650
2 4
$a
Artificial Intelligence.
$3
646849
650
2 4
$a
Mathematics of Computing.
$3
669457
650
2 4
$a
Health Informatics.
$3
593963
650
2 4
$a
Models and Principles.
$3
669634
650
2 4
$a
Operating Systems.
$3
669804
700
1
$a
Chung, Albert C. S.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1226862
700
1
$a
Gee, James C.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1076986
700
1
$a
Yushkevich, Paul A.
$e
editor.
$1
https://orcid.org/0000-0001-8543-4016
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1305809
700
1
$a
Bao, Siqi.
$e
editor.
$1
https://orcid.org/0000-0003-3885-125X
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1305810
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030203504
776
0 8
$i
Printed edition:
$z
9783030203528
830
0
$a
Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
$v
9219
$3
1253644
856
4 0
$u
https://doi.org/10.1007/978-3-030-20351-1
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
912
$a
ZDB-2-LNC
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入