Mark Halling-Brown

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Head of Scientifc Computing (Not a Clinical Scientist)

Royal Surrey County Hospital

United Kingdom

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About

Mark Halling-Brown is the Head of Scientific Computing at the Royal Surrey NHS Foundation Trust in the UK. His research primarily focuses on advancements in breast cancer detection and risk prediction, particularly through the use of MRI and deep learning techniques. He has contributed to the evaluation of diagnostic methods within the NHS and has published extensively on the impact of COVID-19 on health outcomes. His recent work includes studies on mammography systems, national imaging databases, and the effects of long COVID.

Recent Grants

Grant: Close

People-centered Mammogram Analysis

Open Date: 2023-10-02

Close Date: 2025-04-01

Grant: Close

Development and pre-market evaluation of AI-assisted dental disease detection with radiography

Open Date: 2023-06-01

Close Date: 2025-11-30

Grant: Close

Mammographic predictors of cancer recurrence after breast conservation and adjuvant endocrine therapy

Open Date: 2019-03-01

Close Date: 2022-02-28

Articles (10)

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2025

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2024

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2022

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2022

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2022

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2021

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2021

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2021

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2021

Collaborators (25)

Davies Adeloye

University of Edinburgh

UNITED KINGDOM

Mark Halling-Brown

Research Interests

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About

Recent Grants

Articles (10)

FAST MRI: DYAMOND trial protocol (can an abbreviated MRI scan detect breast cancers missed by mammography for screening clients with average mammographic density attending their first screening mammogram?)—a diagnostic yield study within the NHS population-risk breast screening programme

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort

Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study.

Evaluating the effectiveness of abbreviated breast MRI (abMRI) interpretation training for mammogram readers: a multi-centre study assessing diagnostic performance, using an enriched dataset.

The relationship between age of digital mammography systems and number of reported faults and downtime.

Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study

Erratum to: An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis.

An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

Collaborators (25)

Davies Adeloye

Claire Hulme

Kay Por Yip

Neil Hanley

Jacob George

Nazia Chaudhuri

Elizabeth Sage

Huzaifa Adamali

Ryan Thwaites

Michael Gibbons

Karen Piper Hanley

Vicky Kamwa

Joby Cole

Catherine Atkin

Neil Greening

Michael Crooks

Keith M. Channon

Joanna Porter

Jonathan Valabhji

Mark Jones

Pranabashis Haldar

Anil Hormis

Tom Ward

BEN MARSHALL

Melissa Heightman

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