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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

E-BookPDF1 - PDF WatermarkE-Book
243 Seiten
Englisch
Springer International Publishingerschienen am07.03.20241st ed. 2023
Verfügbare Formate
BuchKartoniert, Paperback
EUR53,49
BuchKartoniert, Paperback
EUR53,49
BuchKartoniert, Paperback
EUR53,49
BuchKartoniert, Paperback
EUR53,49
BuchKartoniert, Paperback
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BuchKartoniert, Paperback
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BuchKartoniert, Paperback
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E-BookPDF1 - PDF WatermarkE-Book
EUR60,98

Produkt

Details
Weitere ISBN/GTIN9783031441530
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2024
Erscheinungsdatum07.03.2024
Auflage1st ed. 2023
ReiheLecture Notes in Computer Science
Reihen-Nr.14092
Seiten243 Seiten
SpracheEnglisch
IllustrationenXIX, 243 p. 75 illus., 59 illus. in color.
Artikel-Nr.13544072
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
Applying Quadratic Penalty Method for Intensity-based Deformable Image Registration on BraTS-Reg Challenge 2022.- WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network.- Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients.- 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors.- Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning.- Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation.- MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation.- An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea.-Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation.- Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation.- Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation.- Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation.- A Local Score Strategy for Weight Aggregation in Federated Learning.- Ensemble Outperforms Single Models in Brain Tumor Segmentation.- FeTS Challenge 2022 Task 1: Implementing FedMGDA+ and a new partitioning.- Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client Pruning.- Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation.- Robust Learning Protocol for Federated Tumor Segmentation Challenge.- Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data Distribution.- FedPIDAvg: A PID controller inspired aggregation method for Federated Learning.- Federated Evaluation of nnU-Nets Enhanced with Domain Knowledge for Brain Tumor Segmentation.- Experimenting FedML and NVFLARE for Federated Tumor Segmentation Challenge.mehr

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