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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 1st ed. 2021 [Pehme köide]

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  • Formaat: Paperback / softback, 117 pages, kõrgus x laius: 235x155 mm, kaal: 209 g, 43 Illustrations, color; 4 Illustrations, black and white; VIII, 117 p. 47 illus., 43 illus. in color., 1 Paperback / softback
  • Sari: Image Processing, Computer Vision, Pattern Recognition, and Graphics 13050
  • Ilmumisaeg: 20-Oct-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030898466
  • ISBN-13: 9783030898465
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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 1st ed. 2021Suurem pilt
  • Formaat: Paperback / softback, 117 pages, kõrgus x laius: 235x155 mm, kaal: 209 g, 43 Illustrations, color; 4 Illustrations, black and white; VIII, 117 p. 47 illus., 43 illus. in color., 1 Paperback / softback
  • Sari: Image Processing, Computer Vision, Pattern Recognition, and Graphics 13050
  • Ilmumisaeg: 20-Oct-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030898466
  • ISBN-13: 9783030898465
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030898465.html
Märksõnad:
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.

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.