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Multimodal Learning for Clinical Decision Support: 11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings de Tanveer Syeda-Mahmood

Multimodal Learning for Clinical Decision Support: 11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings: Lecture Notes in Computer Science, cartea 13050

Editat de Tanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard Leahy, Bin Dong, Hongzhi Wang
en Limba Engleză Paperback – 20 oct 2021
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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Specificații

ISBN-13: 9783030898465
ISBN-10: 3030898466
Pagini: 117
Ilustrații: VIII, 117 p. 47 illus., 43 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.19 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

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.