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

Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part VI
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 VI
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
11769
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 VI, 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
  • Intro; Preface; Organization; Accepted MICCAI 2019 Papers; Awards Presented at MICCAI 2018, Granada, Spain; Contents -- Part VI; Computed Tomography; Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma; 1 Introduction; 2 The Segmentation-for-Classification Approach; 2.1 The Overall Framework; 2.2 Training: Multi-scale Deeply-Supervised Segmentation; 2.3 Testing: Coarse-to-Fine Segmentation with Post-processing; 3 Experiments; 3.1 Dataset and Settings; 3.2 Segmentation Results; 3.3 Classification Results; 4 Conclusion; References
  • MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection1 Introduction; 2 Methodology; 2.1 Multi-view FPN; 2.2 Attention Based Feature Aggregation; 2.3 Position-Aware Modeling; 3 Experiments; 3.1 Experimental Setup; 3.2 Comparison with State-of-the-Arts; 3.3 Ablation Study; 4 Conclusion; References; Spatial-Frequency Non-local Convolutional LSTM Network for pRCC Classification; 1 Introduction; 2 Methodology; 2.1 Overview; 2.2 Spatial-Frequency Non-local Convolutional LSTM Network Architecture; 3 Experiments and Results; 3.1 Dataset; 3.2 Pre-processing
  • 3.3 Implementation Details3.4 Results; 4 Conclusion; References; BCD-Net for Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization; 1 Introduction; 2 BCD-Net for Low-Dose CT Reconstruction; 2.1 Architecture; 2.2 Training BCD-Net; 2.3 Convergence Analysis; 2.4 Computational Complexity; 3 Experimental Results and Discussion; 3.1 Experimental Setup; 3.2 Results and Discussion; 4 Conclusions; References; Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning; 1 Introduction; 2 Methods; 2.1 Dataset; 2.2 Data Augmentation
  • 2.3 Value Loss and Cross Entropy Loss Function3 Experiments and Results; 3.1 Evaluation Metrics; 3.2 Semi-supervised Algorithm; 3.3 Double Loss Function Collaborative Training; 4 Conclusion and Discussion; References; Closing the Gap Between Deep and Conventional Image Registration Using Probabilistic Dense Displacement Networks; 1 Introduction and Related Work; 2 Methods; 3 Experimental Validation; 4 Results and Discussions; 5 Conclusion; References; Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning; Abstract; 1 Introduction; 2 Methods
  • 2.1 Prostate Patient Data and Pareto Plan Generation2.2 Deep Learning Architecture; 2.3 Training and Evaluation; 3 Results; 4 Discussion and Conclusion; References; PAN: Projective Adversarial Network for Medical Image Segmentation; 1 Introduction; 2 Method; 2.1 Adversarial Training; 2.2 Segmentor (S); 2.3 Adversarial Networks; 3 Experiments and Results; 4 Conclusion; References; Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction; 1 Introduction; 2 Methodology; 3 Experimental Evaluations; 4 Conclusion; References
Dimensions
unknown
Extent
1 online resource (xxxviii, 860 pages)
File format
unknown
Form of item
online
Isbn
9783030322267
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-3-030-32226-7
  • 10.1007/978-3-030-32
Other physical details
illustrations (some color).
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783030322267
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1123174663
Label
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part VI, 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
  • Intro; Preface; Organization; Accepted MICCAI 2019 Papers; Awards Presented at MICCAI 2018, Granada, Spain; Contents -- Part VI; Computed Tomography; Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma; 1 Introduction; 2 The Segmentation-for-Classification Approach; 2.1 The Overall Framework; 2.2 Training: Multi-scale Deeply-Supervised Segmentation; 2.3 Testing: Coarse-to-Fine Segmentation with Post-processing; 3 Experiments; 3.1 Dataset and Settings; 3.2 Segmentation Results; 3.3 Classification Results; 4 Conclusion; References
  • MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection1 Introduction; 2 Methodology; 2.1 Multi-view FPN; 2.2 Attention Based Feature Aggregation; 2.3 Position-Aware Modeling; 3 Experiments; 3.1 Experimental Setup; 3.2 Comparison with State-of-the-Arts; 3.3 Ablation Study; 4 Conclusion; References; Spatial-Frequency Non-local Convolutional LSTM Network for pRCC Classification; 1 Introduction; 2 Methodology; 2.1 Overview; 2.2 Spatial-Frequency Non-local Convolutional LSTM Network Architecture; 3 Experiments and Results; 3.1 Dataset; 3.2 Pre-processing
  • 3.3 Implementation Details3.4 Results; 4 Conclusion; References; BCD-Net for Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization; 1 Introduction; 2 BCD-Net for Low-Dose CT Reconstruction; 2.1 Architecture; 2.2 Training BCD-Net; 2.3 Convergence Analysis; 2.4 Computational Complexity; 3 Experimental Results and Discussion; 3.1 Experimental Setup; 3.2 Results and Discussion; 4 Conclusions; References; Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning; 1 Introduction; 2 Methods; 2.1 Dataset; 2.2 Data Augmentation
  • 2.3 Value Loss and Cross Entropy Loss Function3 Experiments and Results; 3.1 Evaluation Metrics; 3.2 Semi-supervised Algorithm; 3.3 Double Loss Function Collaborative Training; 4 Conclusion and Discussion; References; Closing the Gap Between Deep and Conventional Image Registration Using Probabilistic Dense Displacement Networks; 1 Introduction and Related Work; 2 Methods; 3 Experimental Validation; 4 Results and Discussions; 5 Conclusion; References; Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning; Abstract; 1 Introduction; 2 Methods
  • 2.1 Prostate Patient Data and Pareto Plan Generation2.2 Deep Learning Architecture; 2.3 Training and Evaluation; 3 Results; 4 Discussion and Conclusion; References; PAN: Projective Adversarial Network for Medical Image Segmentation; 1 Introduction; 2 Method; 2.1 Adversarial Training; 2.2 Segmentor (S); 2.3 Adversarial Networks; 3 Experiments and Results; 4 Conclusion; References; Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction; 1 Introduction; 2 Methodology; 3 Experimental Evaluations; 4 Conclusion; References
Dimensions
unknown
Extent
1 online resource (xxxviii, 860 pages)
File format
unknown
Form of item
online
Isbn
9783030322267
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-3-030-32226-7
  • 10.1007/978-3-030-32
Other physical details
illustrations (some color).
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783030322267
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)1123174663

Library Locations

Processing Feedback ...