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.
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
Prof Karen J Kirkby The University of Manchester / The Christie NHS Foundation Trust
Proton Beam Therapy in Manchester the story so far
Simon Walker-Samuel University College London
Using medical imaging with machine learning to develop efficient tools for diagnosing cancer
Dr Heather Williams The Christie Hospital
State of the Art Positron Emission Tomography
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
Dr. Matthew Prime Roche Diagnostics Information Solutions
Personalized healthcare: Leveraging data to improve patient care through clinical decision support in oncology