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Prof. Tao
Lei
Shaanxi University of Science and Technology, China
Tao Lei is a professor and doctoral supervisor at Shaanxi University of Science
and Technology. He is alos the vice dean of the School of Electronic Information
and Artificial Intelligence, and Senior Member of IEEE/CCF/CSIG. He is selected
from the Shaanxi Provincial High level Talent Program, Shaanxi Provincial
Outstanding Youth, Stanford Top 2% Global Scientists List, etc. He is a deputy
editor, editorial board member, guest editor, etc. for 7 journals, and serves as
conference chairman, technical committee chairman, publicity chairman, reward
committee chairman, branch chairman, etc. in more than 20 international
conferences. His main research areas are computer vision, machine learning, etc.
At present, He has published 4 collections of specialized/authored and
conference papers, and have published over 100 papers in international journals
and conferences such as IEEE TIP, IEEE TMI, IEEE TFS, IEEE TGRS, and IJCAI.
Among them, 9 papers are ESI highly cited papers. His Google Academic Citation
has exceeded 4700. He hosted many projects such as the National Natural Science
Foundation of China (5 projects), Shaanxi Provincial Outstanding Youth Fund, and
Shaanxi Provincial Key Research and Development Program. He won the second prize
of Shaanxi Province Science and Technology Award and the first prize of Gansu
Province Higher Education Research Excellent Achievement Award as the first
complete person.
Speech Title "Semi-supervised Medical Image Segmentation under Limited Labeled Data"
Abstract: Medical image segmentation is a key technology in
the field of intelligent image analysis. At present, a large number of research
results on medical image segmentation have been reported and used for smart
medicine. However, the current mainstream medical image segmentation methods
still face the following challenges. Firstly, accurate segmentation of medical
images is difficult due to high noise and low contrast. Secondly, mainstream
medical image segmentation models have a large number of parameters and slow
inference speed, making it difficult to deploy on low resource devices. Finally,
pixel-level annotation of medical images is very expensive and requires
professional knowledge. To address these problems, our team focuses on
semi-supervised medical image segmentation and its applications. We have
proposed some novelty semi-supervised network models for medical image
segmentation under limited labeled samples, and they show good performance for
some complex tasks.