Dr Sue Astley

University of Manchester

Sue Astley leads research in breast radiomics at the University of Manchester. Her work includes evaluation of novel imaging technologies (CAD, DBT), developing software to detect abnormalities and discover imaging biomarkers, using eye tracking to learn how new imaging systems are best employed, and working closely with radiologists to achieve clinically useful solutions. As breast density lead for the 55,000 strong Predicting Risk of Cancer At Screening (PROCAS) study she has focused her attention on markers of breast cancer risk, harnessing the power of deep learning to exploit the trial data and improve risk prediction.

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Deep learning for breast density assessment and risk prediction

I will review the importance of breast density assessment for cancer risk prediction, identifying women for whom mammography is less effective, and assessing the efficacy of risk reducing interventions. I will then discuss current methods for density assessment, looking at the strengths and limitations of different approaches. Finally, I will describe density assessment using convolutional neural networks, including a review of the issues associated with developing and validating such approaches.

EVEN MORE SEMINARS

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    The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust

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  • Dr Serguei Semenov: Speaking at the European Neurological Convention

    Dr Serguei Semenov
    EMTensor GmbH

    Electromagnetic tomography for brain imaging

  • Peter Ross: Speaking at the European Neurological Convention

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    Improving Quality of Diagnostic PET-CT Reporting: Factors Influencing Doctors’ Performance.

  • David Gibbins: Speaking at the European Neurological Convention

    David Gibbins
    Micrima

    Radio-wave breast imaging a new technology

  • Ernesto A. Cerdena, PhD: Speaking at the European Neurological Convention

    Ernesto A. Cerdena, PhD
    AtlantiCare Regional Medical Center (a member of Geisinger), Atlantic City, New Jersey, USA

    Challenges and Success in Adopting Clinical Decision Support Mechanism In Diagnostic Imaging