An online seminar titled “Beyond breast density: AI-driven breast cancer risk assessment from mammographic images” was given by Dr. Aimilia Gastounioti, on Wednesday 3 November 2021.
The seminar highlighted the crucial contribution of artificial intelligence and technology in the early detection and treatment of breast cancer.
The event was co-organised by the Laboratory of Biomedical Simulations and Imaging (BioSim) of the National Technical University of Athens, in collaboration with the Greece Section of the Institute of Electrical and Electronics Engineers (IEEE Greece Section), the IEEE Engineering in Medicine and Biology Society (EMBS) Greece Chapter, and the IEEE Computational Intelligence Society (CIS) Greece Chapter.
Abstract: Breast cancer risk assessment has become increasingly important for forming tailored breast cancer screening and prevention strategies. An emerging approach to evaluate breast cancer risk more accurately and help better guide personalized patient care is the incorporation of computational imaging phenotypes. In this talk, I will discuss recent results from my research on novel computational approaches that leverage artificial intelligence in breast cancer risk estimation from digital mammograms and tomosynthesis images. First, I will describe computational approaches to breast density estimation. I will then present a novel computational framework which allows breast anatomy to drive localized imaging phenotyping of breast cancer risk. Third, I will discuss the use of cutting-edge deep learning methods to better capture breast parenchymal complexity patterns which are associated with breast cancer risk.
Biography: Aimilia Gastounioti grew up in Athens, Greece. She received her Ph.D. in Engineering from the National Technical University of Athens in 2014. Subsequently, she worked as Postdoctoral Researcher and then as Research Associate in the Department of Radiology at the University of Pennsylvania. Recently she was appointed as Assistant Professor in the Department of Radiology at Washington University in St. Louis, where she is developing a research program on computational imaging analytics and artificial intelligence for cancer screening and risk prediction. She has co-authored 40 peer-reviewed journal articles or book chapters, and more than 60 papers in premier scientific meetings. Among others, she has received research awards from the Susan G. Komen foundation for breast cancer, the Abramson Cancer Center and the Penn Institute for Translational Medicine and Therapeutics.