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Found 6 papers, 2 papers with code
A tutorial on multi-view autoencoders using the multi-view-AE library
Multi-view autoencoders have gained significant traction for their adaptability and versatility in modelling multi-modal data, demonstrating an ability to tailor their approach to suit the characteristics of the data at hand.
Tackling the dimensions in imaging genetics with CLUB-PLS
The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes.
Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities
We aim to develop a multi-modal normative modelling framework where abnormality is aggregated across variables of multiple modalities and is better able to detect deviations than uni-modal baselines.
Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels
When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
At this moment, databanks worldwide contain brain images of previously unimaginable numbers.
