Multimodal learning for clinical decision support : 11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / Tanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard Leahy, Bin Dong, Hongzhi Wang (eds.).

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, Fra...

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Bibliographic Details
Corporate Authors:	ML-CDS (Workshop) Online), International Conference on Medical Image Computing and Computer-Assisted Intervention
Other Authors:	Syeda-Mahmood, Tanveer, Li, Xiang, Madabhushi, Anant, Greenspan, Hayit, Li, Quanzheng, Leahy, Richard M., Dong, Bin (Professor), Wang, Hongzhi
Format:	eBook
Language:	English
Published:	Cham : Springer, 2021.
Series:	Lecture notes in computer science ; 13050. LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics.
Subjects:	Diagnostic imaging > Data processing > Congresses. Computer-assisted surgery > Congresses. Computer-assisted surgery Diagnostic imaging > Data processing Electronic books. proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès.
Online Access:	Click for online access

Table of Contents:

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
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
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
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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