The Doctoral Consortium provides a unique opportunity for students, who are close to finishing or who have recently finished their doctorate degree, to interact with experienced researchers in computer aided medical image processing. The Consortium will designate a chair as mentor, and students can choose any consortium based on their preference or similarity of research interests. All students and mentors will attend a Doctoral Consortium meeting, giving the students an opportunity to discuss their ongoing research and career plans with their mentor. In addition, each student will present a research topic, either describing their thesis research or a single recent paper, to the other participants and their mentors.
Students must be conducting research in medical image processing and be within one year (before or after) of graduating with their doctoral degree.
Students that meet the eligibility requirements should submit an application to firstname.lastname@example.org. You must submit the following as a single pdf file.
- Your CV and list of publications.
- One first-author paper which you are most proud of.
- The title and abstract that you will present at the consortium.
- Three specific questions you have for your mentor.
Please ensure that all pieces of information are included in the application. Incomplete applications will be rejected.
Submission deadline: July 10, 2022
Notification of acceptance: July 15, 2022
Recent trends in deep learning based medical image analysis
Scope and Aim
With the development of artificial intelligence in clinical medicine, medical image deep learning based on ultrasound images, endoscopy images, X-ray angiography has gradually achieved better performance in minimally invasive diagnosis and treatment than traditional medical imaging and experienced doctors. However, the challenging problem caused by low contrast image contents disturbed with the interferences of complex noises and abnormal artifacts, inaccurate and inaccessible labeled training data, heterogenous and indescribable spatiotemporal features, calls for a great breakthrough of methodology in balancing the real time, accuracy and robustness of deep learning performance. In the field of deep learning, on the one hand, model-driven deep learning such as algorithm unrolling has provided an interpretable weakly-supervised deep learning for exploiting the dual advantages of model optimization and data-driven machine learning. On the other hand, visual transformer also has paved the way for a promising methodology. The purpose of this forum is to provide a platform for doctoral students to discuss the frontier development trend of medical image deep learning. Through the introduction and exchange of participants’ representative research work, they can learn and inspire each other and promote the transformation of application scenarios to solve practical clinical problems. The tutor will also share research experience on how to challenge clinical problems and develop independent innovation for career development after graduation.
Mentor: Dr. Baiying Lei, Shenzhen University China
Short bio: Dr. Lei received her M. Eng degree in electronics science and technology from Zhejiang University, China, and 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). She 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 receipt of Distinguished Changjiang Young Scholar of Chinese Ministry of Education, IEEE senior member and has served as Technical Committee members of IEEE Bio 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).