Multimodal Learning for Clinical Decision Support

11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
1. Auflage 2021
ISBN: 978-3-030-89846-5
Verlag: Springer

Buch, Englisch, 117 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 207 g

Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics

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.

Zielgruppe

Research

Autoren/Hrsg.

Syeda-Mahmood, Tanveer

Li, Xiang

Madabhushi, Anant

Greenspan, Hayit

Li, Quanzheng

Leahy, Richard

Dong, Bin

Wang, Hongzhi

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

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.