Ph.D. student in Computer Science at the University of Cincinnati. Graduate research assistant at Imaging Research Center, Cincinnati Children’s Hospital Medical Center. My research area focus on probabilistic representation learning, contrastive and metric learning, and self-supervised learning.
Deep learning; Probabilistic reasoning; Self-supervised learning; Representation learning; Medical imaging analysis.
Radiology, Imaging Research Center
Doctor of Philosophy (Ph.D.)
Computer Science Engineering, University of Cincinnati, Cincinnati, OH
Master of Science (M.S.)
Statistics, Miami University, Oxford, OH 2020
Bachelor of Science (B.S.)
Mathematics, Business Analytics, Miami University, Oxford, OH 2018
A Novel Collaborative Self-Supervised Learning Method for Radiomic Data
Introduction: The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects.