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

doi:10.1109/MLSP.2012.6349809

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

Ophthalmology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 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 language	English (US)
Title of host publication	2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
DOIs	https://doi.org/10.1109/MLSP.2012.6349809
State	Published - 2012
Event	2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain Duration: Sep 23 2012 → Sep 26 2012

Publication series

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

Other

Other	2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Country/Territory	Spain
City	Santander
Period	9/23/12 → 9/26/12

Keywords

feature selection
observer analysis
retinal image analysis

ASJC Scopus subject areas

Human-Computer Interaction
Signal Processing

Access to Document

10.1109/MLSP.2012.6349809

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 Article 6349809 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). https://doi.org/10.1109/MLSP.2012.6349809

Observer and feature analysis on diagnosis of retinopathy of prematurity. / Ataer-Cansizoglu, E.; You, S.; Kalpathy-Cramer, J. et al.
2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012. 2012. 6349809 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ataer-Cansizoglu, E, You, S, Kalpathy-Cramer, J, Keck, K, Chiang, MF & 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, 2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012, Santander, Spain, 9/23/12. https://doi.org/10.1109/MLSP.2012.6349809

Ataer-Cansizoglu E, You S, Kalpathy-Cramer J, Keck K, Chiang MF, Erdogmus D. 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. 2012. 6349809. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP.2012.6349809

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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.",

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Observer and feature analysis on diagnosis of retinopathy of prematurity

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Keywords

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