Machine learning for medical image r...
MLMIR (Workshop) (2021 :)

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  • Machine learning for medical image reconstruction = 4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Machine learning for medical image reconstruction/ edited by Nandinee Haq ... [et al.].
    其他題名: 4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021 : proceedings /
    其他題名: MLMIR 2021
    其他作者: Haq, Nandinee.
    團體作者: MLMIR (Workshop)
    出版者: Cham :Springer International Publishing : : 2021.,
    面頁冊數: viii, 142 p. :ill., digital ;24 cm.
    內容註: Deep Learning for Magnetic Resonance Imaging -- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks -- Efficient Image Registration Network For Non-Rigid Cardiac Motion Estimation -- Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge -- Self-Supervised Dynamic MRI Reconstruction -- A Simulation Pipeline to Generate Realistic Breast Images For Learning DCE-MRI Reconstruction -- Deep MRI Reconstruction with Generative Vision Transformers -- Distortion Removal and Deblurring of Single-Shot DWI MRI Scans -- One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline -- Physics-informed self-supervised deep learning reconstruction for accelerated rst-pass perfusion cardiac MRI -- Deep Learning for General Image Reconstruction -- Noise2Stack: Improving Image Restoration by Learning from Volumetric Data -- Real-time Video Denoising in Fluoroscopic Imaging -- A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution -- Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks.
    Contained By: Springer Nature eBook
    標題: Diagnostic imaging - Congresses. - Data processing -
    電子資源: https://doi.org/10.1007/978-3-030-88552-6
    ISBN: 9783030885526
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