Vor Ort
Merkzettel Warenkorb
  • Produktbild: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
  • Produktbild: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
Band 12587

Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

Aus der Reihe Lecture Notes in Computer Science

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.03.2021

Herausgeber

Nadya Shusharina + weitere

Verlag

Springer

Seitenzahl

156

Maße (L/B/H)

23,5/15,5/1 cm

Gewicht

277 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-71826-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.03.2021

Herausgeber

Verlag

Springer

Seitenzahl

156

Maße (L/B/H)

23,5/15,5/1 cm

Gewicht

277 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-71826-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
  • Produktbild: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
  • ABCs – Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images.- Cross-modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization.- Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread.- Ensembled ResUnet for Anatomical Brain Barriers Segmentation.- An Enhanced Coarse-to-_ne Framework for the segmentation of clinical target volume.- Automatic Segmentation of brain structures for treatment planning optimization and target volume definition.- A Bi-Directional, Multi-Modality Framework for Segmentation of Brain Structures.- L2R – Learn2Reg: Multitask and Multimodal 3D Medical Image Registration.- Large Deformation Image Registration with Anatomy-aware Laplacian Pyramid Networks.- Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge.- Variable Fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg Challenge.- Learning a deformable registration pyramid.- Deep learning based registration using spatial gradients and noisy segmentation labels.- Multi-step, Learning-based, Semi-supervised Image Registration Algorithm.- Using Elastix to register inhale/exhale intrasubject thorax CT: a unsupervised baseline to the task 2 of the Learn2Reg challenge.- TN-SCUI – Thyroid Nodule Segmentation and Classification in Ultrasound Images.- Cascade Unet and CH-Unet for thyroid nodule segmenation and benign and malignant classification.- Identifying Thyroid Nodules in Ultrasound Images through Segmentation-guided Discriminative Localization.- Cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound Images.- Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks.- LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images.- Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation.