Search Results for author:
Found 5 papers, 4 papers with code
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets.
Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work.
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology.
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning
In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI.
Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts.
