With advanced imaging techniques, we are collecting images of various complex structures such as neurons, vessels, tissues and cells. These structures encode important information about underlying biological mechanisms. To fully exploit these structures, we propose to enhance learning pipelines with topology, the branch of abstract mathematics that deals with structures such as connections, loops and branches. Under-the-hood is a formulation of the topological computation as a robust and differentiable operator. This inspires a series of novel methods for segmentation, uncertainty estimation, generation, and analysis of these topology-rich biomedical structures. These methods are applied to various problems in cancer research and neuroscience.
Chao Chen,
Stony Brook University
Chao Chen, Ph.D., is an associate professor in the Department of Biomedical Informatics at Stony Brook University, with affiliated appointments in the departments of Computer Science and Applied Mathematics and Statistics. His research integrates biomedical imaging informatics, robust machine learning, and topological data analysis. He focuses on developing transparent and trustworthy learning methods by combining mathematical modeling with modern deep learning to analyze complex imaging data from pathology and radiology. Prof. Chen has published widely in top-tier venues and has received several honors, including the NSF CAREER Award and the Stony Brook Trustees Faculty Award.