세미나

산업공학과에서는 최신 연구 트렌드를 공유하고 학문적 통찰을 나누는 연구세미나를 개최합니다.

혁신적인 연구에 관심 있는 학부생, 대학원생 여러분의 많은 참여를 환영합니다!

 

📅 세미나 정보

🔸일시 : 2025년 3월 6일(목) 13:00

🔸장소 :  110동 N102호(40명)

🔸발표자 :  Dr. Changgee Chang (Indiana University School of Medicine)

🔸강연주제: Integration of Renography and Expert Ratings for Decision Support System in the Absence of Gold Standard

  – This seminar will be conducted in English

 🔸Zoom: https://unist-ac-kr.zoom.us/j/84011000505?pwd=vr0ovtM0AYs4qgYCacGvL2FP1gpcFb.1

ID: 840 1100 0505

PW: unist1234

 

📑Abstract: 

Kidney obstruction, if untreated in a timely manner, can lead to irreversible loss of renal function. A widely used technology for evaluations of kidneys with suspected obstruction is diuresis renography. However, it is generally very challenging for radiologists who typically interpret renography data in practice to build high level of competency due to the low volume of renography studies and insufficient training. Another challenge is that there is currently no gold standard for detection of kidney obstruction. Seeking to develop a computer-aided diagnostic (CAD) tool that can assist practicing radiologists to reduce errors in the interpretation of kidney obstruction, a recent study collected data from diuresis renography, interpretations on the renography data from highly experienced nuclear medicine experts as well as clinical data. To achieve the objective, we develop a Bayesian latent class model that can be used as a CAD tool for assisting radiologists in kidney interpretation. An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. The superiority of the proposed method over several existing methods is demonstrated through extensive experiments including analysis of real data from renal studies, which lends support to the usefulness of our model as a CAD tool to assist less experienced radiologists in the field.

 

👤Pesenter Bio:

Dr. Changgee Chang is an Assistant Professor in Department of Biostatistics and Health Data Science at Indiana University School of Medicine. He received a PhD in Statistics from University of Chicago. Prior to joining Indiana University, he has been trained as a postdoctoral researcher and a research associate at Emory University and University of Pennsylvania. His research interests include developing statistical methodologies for Alzheimer disease research and integrative analysis of multi-omics and neuroimaging data. He is also interested in developing privacy preserving federated learning algorithms for integrative analysis of multi-site EHR data.