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 machine learning research scientist focused on solving biomedical problems. Currently at Dana Farber Cancer institute, he trains and optimizes deep learning networks for the prognostication and treatment response prediction in lung cancer patients from CT data. In addition to regularly contributing to open-source projects including modelhub.ai and PyRadiomics, he is also intrigued by data visualization, web development, and everything UI/UX. He has previously conducted research at Brigham and Women’s hospital, Wyss Institute for Biologically Inspired Engineering, as well as MIT Media Lab’s Mediated Matter group. As an architect and computational designer in a former life, he spent 4 years working in construction with Foster+Partners in Beijing and Playze in Shanghai. He is currently working on a PhD in machine learning and medical imaging at Maastricht University after having 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.