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[2019-03-15] Dr. Jen-Tang Lu (呂任棠), CEO, Vysioneer, "Deep learning for clinical diagnosis"

專題討論演講公告
Poster:Post date:2019-02-21
Title: Deep learning for clinical diagnosis
Date: 2019-03-15 3:40pm-5:00pm
Location: R102, CSIE
Speaker: Dr. Jen-Tang Lu (呂任棠), CEO, Vysioneer
Hosted by: Prof. Hsuan-Tien Lin
 
 

Abstract:

 
Artificial intelligence (AI) is transforming the healthcare system for better patient care. In this talk, Dr. Lu will discuss how deep learning can streamline clinical workflow and improve healthcare outcomes, with special focus in diagnostic areas. Meanwhile, as a former machine learning scientist at Mass General Hospital and a founder of an AI medical imaging startup, Dr. Lu will share his personal takeaways on AI for healthcare from both clinical and technical viewpoints. Additionally, he will address challenges and approaches that have been taken to translate deep learning to clinical use with examples in radiology, cardiology, and oncology.
 
 
Biography:
 
Dr. Jen-Tang Lu (呂任棠) is a scientist and entrepreneur, who has a strong passion for technological revolution in medical imaging. He is currently a co-founder and the CEO of Vysioneer, an MIT-funded startup company focusing on artificial intelligence for precision medicine. Vysioneer has been developing deep learning-empowered products for radiosurgery and collaborating with leading cancer centers worldwide. Prior to Vysioneer, Dr. Lu was a machine learning scientist in the Center for Clinical Data Science at the Mass General Hospital (MGH). His achievements of automated diagnosis on spine MRI and automated aortic aneurysm detection on CT have been used to facilitate clinical workflow at MGH. Dr. Lu received his Ph.D. in Electrical Engineering from Princeton University, with a focus on algorithm and system development for medical imaging applications. While there, he commercialized his doctoral research on image enhancement for medical ultrasound. The technology was awarded the Top Prize at the Princeton Innovation Forum in 2016.
 
   
Last modification time:2019-02-21 AM 9:44

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