Clinical Translation

Deep learning has shown great promise in the research domain, but significant barriers still exist which limit the clinical translatability of deep learning models. Our research aims to tackle these barriers focusing on the challenges of: distributed data and data privacy, model compression, and the development of clinically meaningful biomarkers.

Challenges for machine learning in clinical translation of big data imaging studies

Dinsdale et al. Neuron (2022)

Distributed Data and Data Privacy

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

Dinsdale et al. bioRxiv (2024)

SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis

Dinsdale et al. ICCV (2023)

FedHarmony: Unlearning Scanner Bias with Distributed Data

Dinsdale et al. MICCAI (2022)

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal

Dinsdale et al. NeuroImage (2021)

Model Compression

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

Dinsdale et al. Medical Image Analysis (2022)

Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment

Vaze et al. IEEE Journal of Biomedical and Health Informatics (2020)

Biomarker Development

Learning patterns of the ageing brain in MRI using deep convolutional networks

Dinsdale et al. NeuroImage (2021)

Is Your Style Transfer Doing Anything Useful? An Investigation Into Hippocampus Segmentation and the Role of Preprocessing

Kalabizadeh et al. MLCN (2024)

Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes

Roibu et al. SDS (2023)