The Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.

This seminar features Professor Jin Tae Kwak from Korea University, Seoul, South Korea.

Abstract

Computational pathology is undergoing a profound transformation driven large-scale artificial intelligence (AI), particularly the seamless integration of multiple data modalities through multimodal large language models (MLLMs). In this talk, I will present our group’s recent efforts in developing knowledge-guided computational pathology tools that utilize vision-language models for various diagnostic tasks. I will discuss how vision-language models, combined with expert knowledge, can aid in analyzing complex tissue patterns, enhancing interpretability, and achieving accurate and reliable diagnostic performance.

Bio

Jin Tae Kwak received his Ph.D. in Computer Science from University of Illinois at Urbana-Champaign, USA in 2012. He earned his Masters of Science degree in Electrical and Computer Engineering from Purdue University, USA in 2007 and his Bachelor of Engineering degree in Electrical Engineering from Korea University, Korea in 2005. In 2012, He joined Center for Interventional Oncology, Clinical Center, National Institutes of Health, USA as a Research Fellow. He served as a tenure-track faculty member in the department of Computer Science and Engineering at Sejong University from March 2016 to August 2020. He joined School of Electrical Engineering at Korea University in September 2020. His research interests include multimodal medical imaging, digital and computational pathology, and large-scale and efficient AI systems for medicine