Machine Learning for Medical Image Reconstruction: Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings: Lecture Notes in Computer Science, cartea 12450
Editat de Farah Deeba, Patricia Johnson, Tobias Würfl, Jong Chul Yeen Limba Engleză Paperback – 20 oct 2020
The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
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Specificații
ISBN-13: 9783030615970
ISBN-10: 3030615979
Pagini: 163
Ilustrații: VIII, 163 p. 76 illus., 48 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.25 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics
Locul publicării:Cham, Switzerland
ISBN-10: 3030615979
Pagini: 163
Ilustrații: VIII, 163 p. 76 illus., 48 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.25 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics
Locul publicării:Cham, Switzerland
Cuprins
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.