Institute of Robotics and Automatic Information System
Tianjin Key Laboratory of Intelligent Robotics
Seminar Series：Advanced Robotics & Artificial Intelligence
报告嘉宾：Lei Li 牛津大学博士后
报告题目：Artificial Intelligence in Cardiac Image Computing and Modeling
Cardiac imaging plays a key role in the management of patients with cardiovascular diseases. Fully automated computing and modelling of cardiac images could be beneficial for clinical research and evidence-based patient management. Deep learning (DL) is widely used in cardiac image analysis, but sometimes DL-based models might fail due to the limited understanding of the data and task. It is essential to deeply understand the data and tasks we have for developing robust models. In this talk, Dr. Lei Li will mainly employ the left atrial LGE MRI computing work as an example, to introduce how to design a specific DL-based model based on the characteristics of the task and data. Additionally, she will also introduce her recent works in multi-modality image computing and cardiac digital twins. The involved techniques in cardiac image analysis, such as image segmentation and registration, multi-modality data integration, multi-center data processing, as well as cardiac simulation and modelling, can be easily extended for other medical image analysis task for precision medicine.
Dr Lei Li is a Postdoctoral Research Assistant at the Institute of Biomedical Engineering, University of Oxford. She obtained her PhD degree from the School of Biomedical Engineering, Shanghai Jiao Tong University in 2021. During her PhD, she was a visiting PhD student at Fudan University and King’s College London, respectively. She obtained the SJTU 2021 Outstanding Doctoral Graduate Development Scholarship. Her research interest is at the interface between machine learning and medical imaging, including developing novel computational methods for medical image analysis as well as translating the methods to clinical research and healthcare. She has already published more than 30 papers with 800+ citations, incl. 6 first/ corresponding authored articles accepted in peer-reviewed journals and 7 first-authored international conference papers. Some of these works have been selected as the most popular and most cited paper in MedIA and IEEE TMI. She is now the Board Member of Women in MICCAI (WiM) and the Editorial Board Member of the Journal of Medical Artificial Intelligence. She has co-organizered four MICCAI challenge events, incl. LAScarQS 2022, MyoPS 2020, MS-CMRSeg 2019, and MM-WHS 2017. She is a reviewer for many journals and conferences, incl. MedIA, IEEE TPAMI, IEEE TMI, IEEE TBME, Neurocomputing, IPMI, ISBI, MIDL, and MICCAI.