Implementing a Healthcare Imaging Pipeline Using Deep Learning
Implementing a Healthcare Imaging Pipeline Using Deep Learning
Bio: Dr. Rahul Remanan is trained as a medical doctor from All India Institute of Medical Sciences in New Delhi, India. He completed his postdoctoral fellowship in Neurology at Cornell University affiliated hospital in New York. Currently he is the CEO of Ekaveda Inc, a private investment management company. Ekaveda focuses on investing in leading edge technology start-ups. He is particularly passionate about developing scalable healthcare solutions. He has since created an enterprise focused Artificial Intelligence company: Moad Computer and a nanotechnology company focused on developing cancer therapeutics: Nanøveda. His interdisciplinary skill sets in medicine and scientific computing is leveraged for facilitating precision medicine by customizing hardware and software. This includes the use of deep learning and cloud computing solutions for healthcare. Broadly, he is interested in building technology driven solutions to bring progress and prosperity to societies around the world. He is a firm believer of technology as an empowering force. He supports the beneficial and benevolent applications of artificial intelligence. Additionally, he is the co-organizer for the New York Healthcare Artificial Intelligence Society (NYHAIS), promoting his vision for AI, technology and its healthcare related applications.
Abstract: Discussion on how to implement a healthcare image analysis pipeline using deep-learning using Moad Computer’s (https://www.moad.computer) cloud connected artificial intelligence platform: Jomiraki. During this event, we will learn the details of how to build a deep-learning pipeline for breast cancer detection. We will discuss transfer learning, image augmentation strategies, GPU computing and layer visualizations. We will also learn the basic API structure and how to build a web-application leveraging these APIs.
Date Mon Apr 16, 2018
Time: 5:30Pm – 9:00PM
Implementing a Healthcare Imaging Pipeline Using Deep Learning