Title: A dual meta-learning framework with application to longitudinally generalized brain tissue segmentation (& other cross-domain segmentation tasks)

Speaker: Chunfeng Lian

Abstract:
Brain tissue segmentation is essential for neuroscience and clinical studies. However, segmentation on (unpaired) longitudinal data is technically challenging due to dynamic brain changes across the lifespan, especially in infancy. In this talk, we’ll introduce a dual meta-learning (DuMeta) paradigm to learn efficiently longitudinally consistent/generalized representations and persist when fine-tuning. Results on heterogenous T1w MRI datasets demonstrate the effectiveness of DuMeta in the challenging one-shot segmentation setting. Also, some further experiments suggest that such a meta-learning strategy could also applied to other cross-domain segmentation tasks.

Speaker Bio:
Chunfeng Lian is currently an Associate Professor of Information Science, School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China. His research focuses on machine learning in medical image computing. He received his PhD in Computer Science from Université de Technologie de Compiègne, CNRS, Heudiasyc (UMR 7253), Compiègne, France, in 2017. From 2017 to 2020, he was a Postdoc with the Radiology department & Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill. Dr. Lian serves as an Associate Editor of three international journals, i.e., Medical Physics, IRBM, and Frontiers in Radiology, and was a Distinguished Reviewer of IEEE T-MI. He was an AC of multiple conferences, e.g., MICCAI’23 & ICPR’22, & a leading co-organizer of MICCAI-MLMI’ 22 workshop.

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