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Multimodal learning for clinical dec...

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Multimodal learning for clinical decision support11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimodal learning for clinical decision supportedited by Tanveer Syeda-Mahmood ... [et al.].
Reminder of title:	11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
remainder title:	ML-CDS 2021
other author:	Syeda-Mahmood, Tanveer.
corporate name:
Published:	Cham :Springer International Publishing :2021.
Description:	viii, 117 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Diagnostic imagingCongresses.Data processing
Online resource:	https://doi.org/10.1007/978-3-030-89847-2
ISBN:	9783030898472$q(electronic bk.)

Multimodal learning for clinical decision support11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
Multimodal learning for clinical decision support11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /[electronic resource] :ML-CDS 2021edited by Tanveer Syeda-Mahmood ... [et al.]. - Cham :Springer International Publishing :2021. - viii, 117 p. :ill., digital ;24 cm. - Lecture notes in computer science,130500302-9743 ;. - Lecture notes in computer science ;4891..

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.

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.

ISBN: 9783030898472$q(electronic bk.)

Standard No.: 10.1007/978-3-030-89847-2doiSubjects--Topical Terms:

445765
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / M5 2021

Dewey Class. No.: 616.0757

Multimodal learning for clinical decision support11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
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520 $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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