Search Results for author:
Found 9 papers, 5 papers with code
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis
Our method outperforms existing SFDA approaches across a range of realistic data scenarios, demonstrating the potential utility of our approach for MRI harmonisation and general SFDA problems.
Segmentation method for cerebral blood vessels from MRA using hysteresis
Due to the absence of open-source tools, we aim to develop a classical segmentation method that generates vessel ground truth from Magnetic Resonance Angiography for DL training of segmentation across a variety of modalities.
FedHarmony: Unlearning Scanner Bias with Distributed Data
The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability.
Uncertainty categories in medical image segmentation: a study of source-related diversity
Measuring uncertainties in the output of a deep learning method is useful in several ways, such as in assisting with interpretation of the outputs, helping build confidence with end users, and for improving the training and performance of the networks.
Mutual information neural estimation for unsupervised multi-modal registration of brain images
We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network.
TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
We tested our method on myocardium segmentation from an open-source 2D heart dataset.
Challenges for machine learning in clinical translation of big data imaging studies
The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology.
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging
Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any lesion changes.
Brain tumour segmentation using a triplanar ensemble of U-Nets
Our method achieved an evaluation score that was the equal 5th highest value (with our method ranking in 10th place) in the BraTS'20 challenge, with mean Dice values of 0. 81, 0. 89 and 0. 84 on ET, WT and TC regions respectively on the BraTS'20 unseen test dataset.
