Deep Learning Opportunities in Cancer Imaging
Radiographic medical images contain a vast wealth of information allowing for accurate non-invasive tumor characterization and ultimately improving cancer diagnosis and care. Recent advances in AI, deep learning in particular, promise to impact multiple facets of the radiology profession and support clinical decision making. The Computational Imaging and Bioinformatics Lab at Dana Farber Cancer Institute and Harvard Medical School is a data science lab focused on the development and application of AI methods on medical data.
In this talk, we will be presenting case studies investigating the clinical utility of deep learning in the detection, segmentation and outcome prediction of cancer tumors. We will also be presenting modelhub.ai, a repository of deep learning models crowdsourced through contributions by the scientific research community.
Ahmed Hosny is a data scientist focused on solving problems with machine learning. Currently at the Dana-Farber Cancer Institute and Harvard Medical School, Ahmed builds deep learning models to extract and explore knowledge from medical images. He is also intrigued by data visualization, distributed learning, computation on encrypted data, and everything open-source. Ahmed previously worked with Harvard’s Wyss Institute for Biologically Inspired Engineering, Brigham and Women’s hospital, MIT Media Lab’s Mediated Matter group, and Foster+Partners as an architect in a former life. He is currently working on a PhD in Machine Learning at Maastricht University. Ahmed completed a Master of Design Studies in Technology at the Harvard Graduate School of Design and a Bachelor of Architecture at the American University of Sharjah.