Simulation and synthesis in medical ...
SASHIMI (Workshop) (2021 :)

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  • Simulation and synthesis in medical imaging = 6th International Workshop, SASHIMI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /
  • Record Type: Electronic resources : Monograph/item
    Title/Author: Simulation and synthesis in medical imaging/ edited by David Svoboda ... [et al.].
    Reminder of title: 6th International Workshop, SASHIMI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /
    remainder title: SASHIMI 2021
    other author: Svoboda, David.
    corporate name: SASHIMI (Workshop)
    Published: Cham :Springer International Publishing : : 2021.,
    Description: x, 154 p. :ill., digital ;24 cm.
    [NT 15003449]: Method-Oriented Papers -- Detail matters: high-frequency content for realistic synthetic brain MRI generation -- Joint Image and Label Self-Super-Resolution -- Super-resolution by Latent Space Exploration: Training with Poorly-aligned Clinical and Micro CT Image Dataset -- A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms -- Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images -- Learning-based Template Synthesis For Groupwise Image Registration -- The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties -- Transfer Learning in Optical Microscopy -- X-ray synthesis based on triangular mesh models using GPU-accelerated ray tracing for multi-modal breast image registration -- Application-Oriented Papers -- Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks -- SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image -- Cerebral Blood Volume Prediction based on Multi-modality Magnetic Resonance Imaging -- Cine-MRI simulation to evaluate tumor tracking -- GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.
    Contained By: Springer Nature eBook
    Subject: Diagnostic imaging - Congresses. - Digital techniques -
    Online resource: https://doi.org/10.1007/978-3-030-87592-3
    ISBN: 9783030875923
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W9409325 電子資源 11.線上閱覽_V 電子書 EB RC78.7.D53 S56 2021 一般使用(Normal) On shelf 0
  • 1 records • Pages 1 •
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