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Analysis of deep complex‐valued convolutional neural networks for MRI reconstruction and phase‐focused applications
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Deep complex convolutional network for fast reconstruction of 3D late gadolinium enhancement cardiac MRI
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DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
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Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification
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Deep Generative Adversarial Neural Networks for Compressive Sensing MRI
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Deep residual dense U-Net for resolution enhancement in accelerated MRI acquisition
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Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Synthesizing Complex-Valued Multicoil MRI Data from Magnitude-Only Images
inferred← citeshttps://doi.org/10.3390/s24030753openalex-snapshot
A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction
inferred← citeshttps://doi.org/10.36227/techrxiv.175339544.49904893/v1openalex-snapshot
Robust Complex-Valued Federated Learning for Secure 6G Mobile Communications
inferred← citeshttps://doi.org/10.55458/neurolibre.00021openalex-snapshot
Leveraging Large Language Models for Interactive Exploration of MRI Research Reproducibility: A Self-Evolving Review
inferred← citeshttps://doi.org/10.58530/2022/4808openalex-snapshot
LeaRning nonlineAr representatIon and projectIon for faSt constrained MRSI rEconstruction (RAIISE)
inferred← citeshttps://doi.org/10.6009/jjrt.2023-2225openalex-snapshot
[MRI] 1. The History of Image Reconstraction for MRI
inferred← citeshttps://doi.org/10.6009/jjrt.2023-2236openalex-snapshot
[MRI] 2. Recent Research on MR Image Reconstruction Using Artificial Intelligence
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560955openalex-snapshot
Additional file 1 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560958openalex-snapshot
Additional file 2 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560961openalex-snapshot
Additional file 3 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560964openalex-snapshot
Additional file 4 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.6084/m9.figshare.26560967openalex-snapshot
Additional file 5 of Artifact-free fat-water separation in Dixon MRI using deep learning
inferred← citeshttps://doi.org/10.70675/cac42885zff54z47eaz848czcfc9a22ac938openalex-snapshot
Robust Deep learning methods inspired by signal processing algorithms
inferred← citeshttps://doi.org/10.70675/fc6628b1z85b2z488czaa64z116a1a677cf4openalex-snapshot
Integrated photonic computing
inferred← citeshttps://doi.org/10.7717/peerj-cs.1903openalex-snapshot
Optimized models and deep learning methods for drug response prediction in cancer treatments: a review
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