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The Resource Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)

Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)

Creator
Editor
Contributor
Summary
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging
Language
eng
Work
Publication
Extent
1 online resource (xxxviii, 888 pages)
Note
  • International conference proceedings
  • Includes author index
Contents
  • Connectivity implicated in AD and MCI
  • Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis
  • Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder
  • Miscellaneous Neuroimaging
  • Doubly Weak Supervision of Deep Learning Models for Head CT
  • Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks
  • FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images
  • Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline
  • Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage
  • Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network
  • Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage
  • Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting fast, consistent tractography segmentation across populations and dMRI acquisitions
  • Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling
  • Optimal experimental design for biophysical modelling in multidimensional diffusion MRI
  • DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
  • Fast and Scalable Optimal Transport for Brain Tractograms
  • A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
  • Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning
  • Functional Neuroimaging (fMRI)
  • Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life
  • A matched filter decomposition of fMRI into resting and task components
  • Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI
  • Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network
  • Invertible Network for Classification and Biomarker Selection for ASD
  • Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data
  • Revealing Functional Connectivity by Learning Graph Laplacian
  • Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks
  • Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale
  • Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net
  • A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI
  • A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion
  • Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis
  • Decoding brain functional
Isbn
9783030322489
Instance
Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III
Title
Medical image computing and computer assisted intervention -- MICCAI 2019
Title remainder
22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings
Title number
Part III
Statement of responsibility
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)
Title variation
MICCAI 2019
Creator
Contributor
Editor
Subject
Genre
Language
eng
Summary
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging
Member of
Cataloging source
GW5XE
Dewey number
616.07/57
Illustrations
illustrations
Index
index present
LC call number
RC78.7.D53
Literary form
non fiction
http://bibfra.me/vocab/lite/meetingDate
2019
http://bibfra.me/vocab/lite/meetingName
International Conference on Medical Image Computing and Computer-Assisted Intervention
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
1948 January 5-
http://library.link/vocab/relatedWorkOrContributorName
  • Shen, Dinggang
  • Liu, Tianming
  • Peters, Terry M.
  • Staib, Lawrence
  • Essert, Caroline
  • Zhou, Xiangyun Sean
  • Yap, Pew-Thian
  • Khan, Ali
Series statement
  • Lecture notes in computer science
  • LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
Series volume
11766
http://library.link/vocab/subjectName
  • Diagnostic imaging
  • Computer-assisted surgery
Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)
Instantiates
Publication
Note
  • International conference proceedings
  • Includes author index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Connectivity implicated in AD and MCI -- Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis -- Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder -- Miscellaneous Neuroimaging -- Doubly Weak Supervision of Deep Learning Models for Head CT -- Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks -- FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images -- Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline -- Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage -- Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network -- Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage -- Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting fast, consistent tractography segmentation across populations and dMRI acquisitions -- Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling -- Optimal experimental design for biophysical modelling in multidimensional diffusion MRI -- DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography -- Fast and Scalable Optimal Transport for Brain Tractograms -- A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes -- Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning -- Functional Neuroimaging (fMRI) -- Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life -- A matched filter decomposition of fMRI into resting and task components -- Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI -- Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network -- Invertible Network for Classification and Biomarker Selection for ASD -- Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data -- Revealing Functional Connectivity by Learning Graph Laplacian -- Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks -- Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale -- Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net -- A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI -- A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion -- Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis -- Decoding brain functional
Dimensions
unknown
Extent
1 online resource (xxxviii, 888 pages)
File format
unknown
Form of item
online
Isbn
9783030322489
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-3-030-32248-9
  • 10.1007/978-3-030-32
Other physical details
illustrations (some color).
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783030322489
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1123174676
Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III, Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.)
Publication
Note
  • International conference proceedings
  • Includes author index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Connectivity implicated in AD and MCI -- Interpretable Feature Learning Using Multi-Output Takagi-Sugeno-Kang Fuzzy System for Multi-center ASD Diagnosis -- Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder -- Miscellaneous Neuroimaging -- Doubly Weak Supervision of Deep Learning Models for Head CT -- Detecting Acute Strokes from Non-Contrast CT Scan Data Using Deep Convolutional Neural Networks -- FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images -- Regression-based Line Detection Network for Delineation of Largely Deformed Brain Midline -- Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage -- Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network -- Recurrent sub-volume analysis of head CT scans for the detection of intracranial hemorrhage -- Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting fast, consistent tractography segmentation across populations and dMRI acquisitions -- Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling -- Optimal experimental design for biophysical modelling in multidimensional diffusion MRI -- DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography -- Fast and Scalable Optimal Transport for Brain Tractograms -- A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes -- Constructing Consistent Longitudinal Brain Networks by Group-wise Graph Learning -- Functional Neuroimaging (fMRI) -- Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life -- A matched filter decomposition of fMRI into resting and task components -- Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-state fMRI -- Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network -- Invertible Network for Classification and Biomarker Selection for ASD -- Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data -- Revealing Functional Connectivity by Learning Graph Laplacian -- Constructing Multi-Scale Connectome Atlas by Learning Common Topology of Brain Networks -- Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale -- Identify Hierarchical Structures from Task-based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net -- A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI -- A Novel Graph Wavelet Model for Brain Multi-Scale Functional-structural Feature Fusion -- Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis -- Decoding brain functional
Dimensions
unknown
Extent
1 online resource (xxxviii, 888 pages)
File format
unknown
Form of item
online
Isbn
9783030322489
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-3-030-32248-9
  • 10.1007/978-3-030-32
Other physical details
illustrations (some color).
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783030322489
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1123174676

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