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Found 6 papers, 0 papers with code
DuDoUniNeXt: Dual-domain unified hybrid model for single and multi-contrast undersampled MRI reconstruction
Multi-contrast (MC) Magnetic Resonance Imaging (MRI) reconstruction aims to incorporate a reference image of auxiliary modality to guide the reconstruction process of the target modality.
Evidence-aware multi-modal data fusion and its application to total knee replacement prediction
For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch.
Undersampled MRI Reconstruction with Side Information-Guided Normalisation
In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction.
Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure.
Universal Undersampled MRI Reconstruction
Simply mixing images from multiple anatomies for training a single network does not lead to an ideal universal model due to the statistical shift among datasets of various anatomies, the need to retrain from scratch on all datasets with the addition of a new dataset, and the difficulty in dealing with imbalanced sampling when the new dataset is further of a smaller size.
Extraction of Behavioral Features from Smartphone and Wearable Data
The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild.
