應中法生物醫學信息研究中心與江蘇省醫學信息處理國合實驗室邀請，12月17日（周五）晚上19:00 –20:00，英國帝國理工大學 楊光研究員給bet356手机版唯一官网師生做學術報告，具體情況如下：
報告題目：Data-Driven Deep Learning Frameworks for Cardiac MRI
報告人：英國帝國理工大學 楊光 研究員
北京時間：2021年12月17日（周五）晚上19:00 – 20:00
地點：bet356手机版唯一官网四牌樓校區群賢樓影像實驗室會議室
Zoom Meeting 鍊接：
Topic: Webinar at Southeast University
Time: Dec 17, 2021 11:00 AM London
Join Zoom Meeting
https://imperial-ac-uk.zoom.us/j/94226569842?pwd=YXB6NUZQdnNCY04raUVJRGErTFhnUT09
Meeting ID: 942 2656 9842
Passcode: P9N1i^
摘要：
In recent years, major advancements have been made in Artificial Intelligence (AI), which are rising in sophistication, complexity and autonomy. A continually veritable and explosive data growth with a rapid iteration of the innovation of computer hardware provides a turbo boost for AI development. AI is an overarching term in computer science and an umbrella concept that provides means to imbue machines with human-like “general” intelligence with minimal human intervention. It encompasses a wide variety of research studies, from computer vision, natural language processing, and robotics to medical data analysis, including both theoretical and practical development of machine learning and newly rebranded and prosperous deep learning. Cardiovascular disorders are the leading cause of death and morbidity worldwide. AI approaches, in particular, deep learning, are especially suited to solving the problems of scalability and high data dimensionality and are showing great potential in the research of cardiac image analysis; however, imaging technology and image quality are the main hurdles for downstream analysis. In this talk, I will share some of the insights in novel imaging technologies, e.g., advanced cardiac MRI and fast imaging using data-driven deep learning frameworks.
報告人簡曆：
Dr Guang Yang is a Future Leaders Fellow (Tenured Senior Research Fellow) in the National Heart and Lung Institute at Imperial College London. He is also an Honorary Senior Lecturer in the School of Biomedical Engineering & Imaging Sciences at King's College London. His research group is interested in developing novel and translational techniques for imaging and biomedical data analysis. His group focuses on the research and development on data-driven fast imaging, data harmonisation, image segmentation, image synthesis, federated learning, explainable AI etc. He is currently working on a wide range of clinical applications in cardiovascular disease, lung disease and oncology. Read more information about Yang’s Lab at: https://www.yanglab.fyi/