Projects

PyKale: an ML Library in PyTorch Ecosystem
Make machine learning more accessible to interdisciplinary research by bridging gaps between data, software, and end users
PyKale: an ML Library in PyTorch Ecosystem
Uncertainty Est. for Landmark Localisation
Quantify the uncertainty in automatic anatomical landmark localisation
Uncertainty Est. for Landmark Localisation
Low-Bias Evaluation of Drug-Target Interaction
Hierarchical clustering split for low-bias evaluation of drug-target interaction prediction
Low-Bias Evaluation of Drug-Target Interaction
Human-In-The-Loop DL for Cardiac CT/MRI
Interactively trained ‘human-in-the-loop’ deep learning to improve cardiac CT/MRI assessment for accurate therapy response and mortality prediction
Human-In-The-Loop DL for Cardiac CT/MRI
Mixed-order Spectral Clustering for Networks
Model both second-order and third-order structures simultaneously for complex networks
Mixed-order Spectral Clustering for Networks
Multisite Brain fMRI Classification
Multi-site autism classification based on site-dependence minimisation and second-order functional connectivity
Multisite Brain fMRI Classification
Interpretable ML for Cardiac MRI
Interpretable machine learning to improve prognostic and treatment response assessment on cardiac MRI
Interpretable ML for Cardiac MRI
Learnable GCN Aggregator
A feature-importance-aware and robust aggregator for graph convolutional networks (GCNs)
Learnable GCN Aggregator
Learn via Tensor Model
Learn low-dimensional representations of high-dimensional data from their natural tensors
Learn via Tensor Model