Machine Learning for Medical Image Reconstruction

Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

Häftad, Engelska, 2020

Av Farah Deeba, Patricia Johnson, Tobias Würfl, Jong Chul Ye, Tobias Wurfl

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This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.

Produktinformation

Utgivningsdatum2020-10-20
Mått155 x 235 x 10 mm
Vikt271 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor163
FörlagSpringer Nature Switzerland AG
ISBN9783030615970

Tillhör följande kategorier

Deep Learning for Magnetic Resonance Imaging.- 3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI.- Deep Parallel MRI Reconstruction Network Without Coil Sensitivities.- Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data.- Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI.- Model-based Learning for Quantitative Susceptibility Mapping.- Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks.- Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping.- Data-Consistency in Latent Space and Online Update Strategy to Guide GAN for Fast MRI Reconstruction.- Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI.- AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesis.- Deep Learning for General Image Reconstruction.- A deep prior approach to magnetic particle imaging.- End-To-End Convolutional NeuralNetwork for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images.- Cellular/Vascular Reconstruction using a Deep CNN for Semantic Image Preprocessing and Explicit Segmentation.- Improving PET-CT Image Segmentation via Deep Multi-Modality Data Augmentation.- Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning.