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Found 7 papers, 2 papers with code
A latent space for unsupervised MR image quality control via artifact assessment
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis.
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.
Self domain adapted network
However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or source data are unavailable due to data privacy.
Evaluating the Impact of Intensity Normalization on MR Image Synthesis
Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image.
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image.
