Buch, Englisch, 89 Seiten, Format (B × H): 155 mm x 235 mm
International Challenge, Learn2Reg 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27-October 4, 2025, Proceedings
Buch, Englisch, 89 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-032-25168-8
Verlag: Springer Nature Switzerland AG
This book constitutes the refereed proceedings of the International Challenge on Medical Image Registration, Learn2Reg 2025, held in conjunction with MICCAI 2025, which took place in Daejeon, South Korea, during September 27–October 4, 2025.
The 11 full papers presented in the proceedings were carefully selected and reviewed from 38 submissions. The papers cover two principle sub-tasks: ReMIND2Reg and LUMIR.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
Weitere Infos & Material
.- ReMIND2Reg.
.- Unsupervised MR-US Multimodal Image Registration with Multilevel Correlation Pyramidal Optimization.
.- In Gradients We Trust: NGF-Driven Registration for ReMIND 2025.
.- Gabor-Based Neighborhood Descriptor for MRI–iUS Brain Image Registration.
.- LUMIR.
.- Zero-shot Multi-Contrast Brain MRI Registration by Intensity Randomizing T1-weighted MRI (LUMIR25).
.- Strategies for Robust Deep Learning Based De formable Registration.
.- Unleashing the power of intensity augmentation for multi-modal image registration.
.- Adapting Frozen Mono-modal Backbones for Multi modal Registration via Contrast-Agnostic Instance Optimization.
.- Generalizable Learning-based Image Registration via Self-Supervised Multi-modal Representation Learning from Single-modal Data.
.- Swin-CNN Hybrid Framework for Enhanced De formable Image Registration.
.- Efficient Unsupervised Multimodal Brain MR Image Registration with Encoder-only Network.
