Ana I. L. Namburete

UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data

UniFed addresses harmonisation and privacy in neuroimaging by enabling federated learning with partially labelled distributed data, producing high-quality segmentations.

STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation

STAMP trains and prunes a UNet for medical image segmentation with online pruning, achieving higher performance and reducing parameters by over 85% with fewer labels.

Subcortical Segmentation of the Fetal Brain in 3D Ultrasound using Deep Learning

The paper develops a CNN for automated segmentation of fetal brain structures in 3D ultrasound using few-shot learning, achieving performance close to intra-observer variability with minimal manual annotations.

Deep Learning-Based Unlearning of Dataset Bias for MRI Harmonisation and Confound Removal

This paper introduces a deep learning framework to reduce scanner bias and other confounds in MRI data, improving harmonization and generalizability across datasets.

Learning Patterns of the Ageing Brain in MRI using Deep Convolutional Networks

This paper uses a 3D CNN to predict brain age from MRI data and finds that prediction errors correlate with clinical measurements and image-derived phenotypes.