Séminaire au DIC: «Deep learning approaches to medical applications»

Voici l’annonce du 3e séminaire au DIC pour la session HIVER 2018  par Joseph Paul COHEN

Titre : Deep learning approaches to medical applications

Résumé

This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.

Biographie

Joseph Paul Cohen is a Postdoctoral Fellow at the Montreal Institute for Learning Algorithms working on medical applications of deep learning including computer vision and genomics. He holds a Ph.D Degree in Computer Science and Machine Learning from the University of Massachusetts Boston. His research interests include machine learning, computer vision, ad-hoc networking, and cyber security. Joseph received a U.S. National Science Foundation Graduate Fellowship in 2013 as well as COSPAR’s Outstanding Paper Award for Young Scientists in the same year. Joseph is the founder of the Institute for Reproducible Research which produces ShortScience.org; which lets researchers publish and read summaries of research papers like an online journal club, as well as Academic Torrents; a system designed  to move large datasets and become the library of the future. He is also the creator of BlindTool; a mobile application providing a sense of vision to the blind by using an artificial neural network that speaks names of objects as they are identified. Joseph is the creator of Blucat; a cross-platform Bluetooth debugging tool. He has worked in industry for small startups, large corporations, government research labs, educational museums, as well as been involved in projects sponsored by NASA and the DOE.