Machine learning for medical image r...
MLMIR (Workshop) (2019 :)

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  • Machine learning for medical image reconstruction = second International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : 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 Florian Knoll ... [et al.].
    其他題名: second International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
    其他題名: MLMIR 2019
    其他作者: Knoll, Florian.
    團體作者: MLMIR (Workshop)
    出版者: Cham :Springer International Publishing : : 2019.,
    面頁冊數: ix, 266 p. :ill. (some col.), digital ;24 cm.
    內容註: Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction.
    Contained By: Springer eBooks
    標題: Machine learning - Congresses. -
    電子資源: https://doi.org/10.1007/978-3-030-33843-5
    ISBN: 9783030338435
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