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|2 doi
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|a HCDD
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| 111 |
2 |
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|a ML-CDS (Workshop)
|n (11th :
|d 2021 :
|c Online)
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| 245 |
1 |
0 |
|a Multimodal learning for clinical decision support :
|b 11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings /
|c Tanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard Leahy, Bin Dong, Hongzhi Wang (eds.).
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| 246 |
3 |
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|a ML-CDS 2021
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| 260 |
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|a Cham :
|b Springer,
|c 2021.
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| 300 |
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|a 1 online resource (125 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
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| 347 |
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|b PDF
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| 490 |
1 |
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|a Lecture notes in computer science ;
|v 13050
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| 490 |
1 |
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|a LNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
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| 500 |
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|a "Which was held virtually at MICCAI 2021"--Preface
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| 505 |
0 |
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|a Intro -- Preface -- Organization -- Contents -- From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data -- 1 Introduction -- 2 Methods -- 2.1 Image Acquisition and Processing -- 2.2 Algorithm for Multimodal Registration with Damaged Tissue -- 2.3 Scattering Transform for Retaining High Resolution Texture -- 2.4 Varifold Measures for Modeling and Crossing Multiple Scales -- 3 Results -- 4 Discussion -- References -- Multi-scale Hybrid Transformer Networks: Application to Prostate Disease Classification
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| 505 |
8 |
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|a 1 Introduction -- 1.1 Contribution -- 2 Methods -- 2.1 Model Comparison -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Pre-processing and Augmentation -- 3.3 Training -- 3.4 Results -- 4 Conclusion -- References -- Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support -- 1 Introduction -- 2 Methods -- 2.1 Pipeline Overview -- 2.2 Dataset and Ground Truth Annotation -- 2.3 Automated Segmentation -- 2.4 Therapy Response Prediction -- 3 Results -- 3.1 Segmentation -- 3.2 Therapy Response Prediction -- 4 Discussion -- 5 Conclusion
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| 504 |
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|a References-A Federated Multigraph Integration Approach for Connectional Brain Template Learning-1 Introduction-2 Proposed Method-3 Results and Discussion-4 Conclusion-References-SAMA: Spatially-Aware Multimodal Network with Attention For Early Lung Cancer Diagnosis-1 Introduction-2 Method-2.1 SAMA Module-3 Experimental Setup-3.1 Dataset-3.2 Implementation Details-4 Results-4.1 Control Experiments-5 Conclusions-References-Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT-1 Introduction-2 Methods-2.1 Datasets.
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| 505 |
8 |
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|a 2.2 UNet Architecture and Training -- 2.3 Radiomics Workflow -- 3 Results -- 3.1 GTVt Segmentation -- 3.2 Prognosis Prediction -- 4 Discussion and Conclusions -- References -- Feature Selection for Privileged Modalities in Disease Classification -- 1 Introduction -- 2 Background -- 2.1 Learning Using Privileged Information -- 2.2 Mutual Information Feature Selection -- 3 Method -- 4 Experiments -- 4.1 Compared LUPI Models -- 4.2 Datasets -- 5 Results -- 5.1 Parkinson's Dataset -- 5.2 TMJ Osteoarthritis Dataset -- 6 Conclusions -- References
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| 505 |
8 |
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|a Merging and Annotating Teeth and Roots from Automated Segmentation of Multimodal Images -- 1 Introduction -- 2 Materials -- 3 Related Work -- 3.1 Root Canal Segmentation Algorithm -- 3.2 3D Shape Analysis for Segmentation and Classification -- 4 Methods -- 4.1 Root Canal Segmentation Algorithm -- 4.2 Dental Model Segmentation Algorithm -- 4.3 Universal Labeling and Merging Algorithm -- 5 Results -- 5.1 Root Canal Segmentation -- 5.2 Universal Labeling and Merging Algorithm -- 6 Conclusion -- References -- Structure and Feature Based Graph U-Net for Early Alzheimer's Disease Prediction
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| 500 |
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|a 1 Introduction.
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| 500 |
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|a Includes author index.
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| 520 |
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|a This book constitutes the refereed joint proceedings of the 11th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2021, held in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in October 2021. The workshop was held virtually due to the COVID-19 pandemic. The 10 full papers presented at ML-CDS 2021 were carefully reviewed and selected from numerous submissions. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed November 3, 2021).
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| 650 |
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0 |
|a Diagnostic imaging
|x Data processing
|v Congresses.
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| 650 |
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0 |
|a Computer-assisted surgery
|v Congresses.
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| 650 |
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7 |
|a Computer-assisted surgery
|2 fast
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| 650 |
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7 |
|a Diagnostic imaging
|x Data processing
|2 fast
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| 655 |
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0 |
|a Electronic books.
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| 655 |
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7 |
|a proceedings (reports)
|2 aat
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
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7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
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7 |
|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Syeda-Mahmood, Tanveer.
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| 700 |
1 |
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|a Li, Xiang.
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| 700 |
1 |
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|a Madabhushi, Anant.
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| 700 |
1 |
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|a Greenspan, Hayit.
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| 700 |
1 |
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|a Li, Quanzheng.
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| 700 |
1 |
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|a Leahy, Richard M.
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| 700 |
1 |
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|a Dong, Bin
|c (Professor)
|1 https://id.oclc.org/worldcat/entity/E39PCjHvHvB6tYQwfJXhFk9Pw3
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| 700 |
1 |
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|a Wang, Hongzhi.
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| 711 |
2 |
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|a International Conference on Medical Image Computing and Computer-Assisted Intervention
|n (24th :
|d 2021 :
|c Online)
|
| 758 |
|
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|i has work:
|a Multimodal learning for clinical decision support (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGMpvMVKHhVt4qxKkQXVQ3
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Syeda-Mahmood, Tanveer.
|t Multimodal Learning for Clinical Decision Support.
|d Cham : Springer International Publishing AG, ©2021
|z 9783030898465
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 13050.
|
| 830 |
|
0 |
|a LNCS sublibrary.
|n SL 6,
|p Image processing, computer vision, pattern recognition, and graphics.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-89847-2
|y Click for online access
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| 903 |
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|a SPRING-COMP2021
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| 994 |
|
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|a 92
|b HCD
|