Multimodal Learning for Clinical Decision Support (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
117
Utgivningsdatum
2021-10-20
Upplaga
1st ed. 2021
Förlag
Springer Nature Switzerland AG
Medarbetare
Syeda-Mahmood, Tanveer (ed.), Li, Xiang (ed.), Madabhushi, Anant (ed.), Wang, Hongzhi (ed.), Li, Quanzheng (ed.), Leahy, Richard (ed.), Dong, Bin (ed.), Greenspan, Hayit (ed.)
Illustrationer
43 Illustrations, color; 4 Illustrations, black and white; VIII, 117 p. 47 illus., 43 illus. in colo
Dimensioner
234 x 156 x 7 mm
Vikt
191 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030898465
Multimodal Learning for Clinical Decision Support (häftad)

Multimodal Learning for Clinical Decision Support

11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

Häftad Engelska, 2021-10-20
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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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Innehållsförteckning

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.