Observer and Feature Analysis on Diagnosis of Retinopathy of Prematurity
Lead Presenter: Esra Ataer-Cansizoglu
Additional Presenters: Sheng You, Jayashree Kalpathy-Cramer, Katie Keck, Michael F. Chiang, Deniz Erdogmus
Faculty Advisor/Principal Investigator: Deniz Erdogmus
Method of Presentation: Poster
Retinopathy of prematurity (ROP) is a disease affecting low-birth weight infants and is a major cause of childhood blindness. However, human diagnoses is often subjective and qualitative. We propose a method to analyze the variability of expert decisions and the relationship between the expert diagnoses and features. The analysis is based on Mutual Information and Kernel Density Estimation on features. The experiments are carried out on a dataset of 34 retinal images diagnosed by 22 experts. The results show that a group of observers decide consistently with each other and there are popular features that have a high correlation with labels.