Multimodal Learning for Clinical Decision Support
11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
Published on 20. October 2021
Book
Paperback/Softback
VIII, 117 pages
978-3-030-89846-5 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
43 farbige Abbildungen, 4 s/w Abbildungen
VIII, 117 p. 47 illus., 43 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 8 mm
Weight
207 gr
ISBN-13
978-3-030-89846-5 (9783030898465)
DOI
10.1007/978-3-030-89847-2
Schweitzer Classification
Other editions
Additional editions
Tanveer Syeda-Mahmood | Xiang Li | Anant Madabhushi
Multimodal Learning for Clinical Decision Support
11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
E-Book
10/2021
Springer
€58.84
Available for download
Content
From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data.- Multi-Scale Hybrid Transformer Networks: Application to Prostate Disease Classification.- Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support.- A Federated Multigraph Integration Approach for Connectional Brain Template Learning.- SAMA: Spatially-Aware Multimodal Network with Attention for Early Lung Cancer Diagnosis.- Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT.- Feature Selection for Privileged Modalities in Disease Classification.- Merging and Annotating Teeth and Roots from Automated Segmentation of Multimodal Images.- Structure and Feature based Graph U-Net for Early Alzheimer's Disease Prediction.- A Method for Predicting Alzheimer's Disease based on the Fusion of Single Nucleotide Polymorphisms and Magnetic Resonance Feature Extraction.