AIMRC Seminar: A Guide for Open-Source Deep Learning Tools in Bioimaging

University Relations

Professor Xintao Wu

The Arkansas Integrative Metabolic Research Center will host Xintao Wu, professor of computer science and computer engineering, at 11 a.m. Wednesday, Dec. 7, via Zoom. Wu will discuss current and potential bioimage analysis problems, and several deep learning platforms used within the bioimage analysis community to approach these challenges.

The bioimage analysis community has adopted and developed several platforms, such as ImageJ, Fiji and ilastik, which use conventional, non-deep-learning-based machine learning approaches in bioimage analysis problems. Following the rising popularity and success of deep learning in the past decade, some of these platforms have been further developed to include DL-based algorithms, and new platforms have also emerged.

In this talk, Wu will first overview selected available open-source DL-based resources, including DeepImageJ, CSBDeep, ZeroCostDL4Mic and Bioimage Model Zoo. Several of these resources offer the direct use of pre-trained DL models for bioimaging without the need for coding expertise by users; offer some support training using an existing model with users' own data, either from scratch or by fine-tuning a pre-trained model; and others support experienced programmers via freely available Python libraries. Wu will demonstrate using these resources for bioimage analysis tasks — image restoration, image segmentation and image quantification and tracking. He will conclude the talk with challenges and some future research directions.

Wu is a professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in the Department of Computer Science and Computer Engineering at the U of A. He currently serves as the data science core director in the Arkansas Integrative Metabolic Research Center. He was a faculty member in the College of Computing and Informatics at the University of North Carolina at Charlotte from 2001 to 2014. He earned his B.S. degree in information science from the University of Science and Technology of China in 1994, an M.E. degree in computer engineering from the Chinese Academy of Space Technology in 1997 and a Ph.D. in information technology from George Mason University in 2001.

Wu's major research interests include data mining, privacy and security, fairness aware learning and big data analysis. Wu has published over 150 scholarly papers and served on editorial boards of several international journals and many program committees of top international conferences in data mining and AI. Wu is also a recipient of NSF CAREER Award (2006) and several paper awards including PAKDD'13 Best Application Paper Award, BIBM'13 Best Paper Award, CNS'19 Best Paper Award and PAKDD'19 Most Influential Paper Award.

This event is supported by the NIGMS of the National Institutes of Health under award number P20GM139768. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.