Observer and feature analysis on diagnosis of retinopathy of prematurity

E. Ataer-Cansizoglu, S. You, J. Kalpathy-Cramer, K. Keck, M. F. Chiang, D. Erdogmus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
DOIs
StatePublished - Dec 12 2012
Event2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain
Duration: Sep 23 2012Sep 26 2012

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
CountrySpain
CitySantander
Period9/23/129/26/12

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Keywords

  • feature selection
  • observer analysis
  • retinal image analysis

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

Cite this

Ataer-Cansizoglu, E., You, S., Kalpathy-Cramer, J., Keck, K., Chiang, M. F., & Erdogmus, D. (2012). Observer and feature analysis on diagnosis of retinopathy of prematurity. In 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012 [6349809] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). https://doi.org/10.1109/MLSP.2012.6349809