Buch, Englisch, Band 13587, 157 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 265 g
5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Buch, Englisch, Band 13587, 157 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 265 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-17246-5
Verlag: Springer International Publishing
The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Deep Learning for Magnetic Resonance Imaging.- Rethinking the optimization process for self-supervised model-driven MRI reconstruction.- NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data.- Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations.- High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors.- Metal artifact correction MRI using multi-contrast deep neural networks for diagnosis of degenerative spinal diseases.- Segmentation-Aware MRI Reconstruction.- MRI Reconstruction with Conditional Adversarial Transformers.- Deep Learning for General Image Reconstruction- A Noise-level-aware Framework for PET Image Denoising.- DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction.- Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, and S. Kevin Zhou Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects.- PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction.- Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging.- DPDudoNet: Deep-Prior based Dual-domain Network for Low-dose Computed Tomography Reconstruction.- MTD-GAN: Multi-Task Discriminator based Generative Adversarial Networks for Low-Dose CT Denoising.- Uncertainty-Informed Bayesian PET Image Reconstruction using a Deep Image Prior.