Interpretable Machine Learning

PyKale: an ML Library in PyTorch Ecosystem

Make machine learning more accessible to interdisciplinary research by bridging gaps between data, software, and end users

Haiping Lu, Xianyuan Liu, Robert Turner, Shuo Zhou, Peizhen Bai, Raivo Koot, Lawrence Schobs

PyKale: an ML Library in PyTorch Ecosystem

Uncertainty Est. for Landmark Localisation

Quantify the uncertainty in automatic anatomical landmark localisation

Lawrence Schobs, Andrew Swift, Haiping Lu

Uncertainty Est. for Landmark Localisation

Mixed-order Spectral Clustering for Networks

Model both second-order and third-order structures simultaneously for complex networks

Yan Ge, Haiping Lu

Mixed-order Spectral Clustering for Networks

Multisite Brain fMRI Classification

Multi-site autism classification based on site-dependence minimisation and second-order functional connectivity

Shuo Zhou, Gaolang Gong, Haiping Lu

Multisite Brain fMRI Classification

Interpretable ML for Cardiac MRI

Interpretable machine learning to improve prognostic and treatment response assessment on cardiac MRI

Samer Alabed, Shuo Zhou, Johanna Uthoff, Andrew Swift, Haiping Lu

Interpretable ML for Cardiac MRI

Learnable GCN Aggregator

A feature-importance-aware and robust aggregator for graph convolutional networks (GCNs)

Li Zhang, Haiping Lu

Learnable GCN Aggregator

Learn via Tensor Model

Learn low-dimensional representations of high-dimensional data from their natural tensors

Learn via Tensor Model