Upcoming SPS BISP TC Webinar: 7 June 2022, by Dr. Baiying Lei

IEEE SPS BISP TC Webinar
(Organized by the IEEE SPS Biomedical Imaging and Signal Processing Technical Committee (BISP TC))

Title: Multi-Modal and Multi-Time Neuro-Imaging Learning for Brain Disease Analysis
Date: 7 June 2022
Time: 12:00 PM - 01:00 PM (Central European Time/CET) | Local time
Duration: Approximately 1 Hour
Presenters: Dr. Baiying Lei

Register for the webinar via WebEx!

About the topic:

Neuro-imaging is safe and non-invasive, which is widely used in the early diagnosis and prediction of Alzheimer's disease, Parkinson's disease, Autism and other brain diseases. With the rapid development of intelligent computing technology, it is possible to use neuro-imaging data for intelligent diagnosis of brain diseases. Starting from the clinical needs of brain diseases, we have carried out the research on intelligent diagnosis and prediction of brain diseases through multi-modal and multi-time point neuroimage data, and have achieved some preliminary results, which have become a complete research system and technical chain. Guided by the practical problems in clinical diagnosis, we propose a deep learning algorithm for neuroimaging data, and put the final research results into clinical practice. We focus on machine learning, deep learning and data mining algorithms in the field of computer vision, and carry out systematic research on the early diagnosis of brain diseases: 1) a series of feature learning algorithms are proposed to solve the problems of high specificity, large individual differences and high dimension of neuroimaging in patients with Alzheimer's disease, Parkinson's disease, Autism, and other brain diseases; 2) To explore the construction of brain network and explore the internal relationship between brain function degradation and brain activation; 3) based on deep learning and machine learning, the early diagnosis model of brain diseases is established to improve the accuracy of diagnosis and prediction.
 

About the presenter:

Baiying Lei received the M.Eng. degree in electronics science and technology from Zhejiang University, China, and the Ph.D. degree from Nanyang Technological University (NTU), Singapore, respectively.

Her research interests include medical image analysis, artificial intelligence, and pattern recognition. In these areas, she has published more than 200 scientific articles in refereed international journals such as the IEEE Transactions on Medical Imaging, Medical Image Analysis, IEEE Transactions on Cybernetics, and IEEE Transactions on Biomedical Engineering; and conference proceedings such as AAAI and MICCAI. Among them, more than 102 SCI journals have been published as the first author or corresponding author (32 IEEE, 35 as the first authors, 56 as the corresponding author, 20 IEEE Transactions, 1 ESI).

Dr. Lei has 14 authorized patents as the first inventor. She has served more than 30 journals as a reviewer. She has been a principal investigator of 18 grants including national science foundation of China (NSFC), Ministry of Science and Technology (MOST), NSFC of Guangdong province. She has obtained the first prize of Shenzhen Science and Technology Award, and third prize of Wenjun Wu Artificial Intelligence Science and Technology Award. She is a recipient of the Distinguished Changjiang Young Scholar of Chinese Ministry of Education, she is an IEEE Senior Member, and has served as Technical Committee members of IEEE-SPS Biomedical Imaging Signal Processing (BISP) and IEEE Biomedical Imaging and Image Processing (BIIP), program committee member for several international and native conferences such as IJCAI and AAAI. She is a MICCAI member, AAAI member, SPIE member. She is a committee member of the Machine Learning Society of the Chinese Association of Artificial Intelligence (CAAI), and the Artificial Intelligence & Pattern Recognition Society of the China Computer Federation (CCF). She currently serves as the Associator Editor of IEEE Transactions on Medical Imaging (IEEE-TMI), IEEE Transactions on Neural Networks and Learning Systems (IEEE-TNNLS), editorial board member of Medical Image Analysis (MedIA).