Observer and feature analysis on diagnosis of retinopathy of prematurity

E. Ataer-Cansizoglu, S. You, Jayashree Kalpathy-Cramer, K. Keck, Michael Chiang, D. Erdogmus

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

7 Citations (Scopus)

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 publicationIEEE International Workshop on Machine Learning for Signal Processing, MLSP
DOIs
StatePublished - 2012
Event2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain
Duration: Sep 23 2012Sep 26 2012

Other

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

Fingerprint

Labels
Experiments

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., & Erdogmus, D. (2012). Observer and feature analysis on diagnosis of retinopathy of prematurity. In IEEE International Workshop on Machine Learning for Signal Processing, MLSP [6349809] 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, Jayashree; Keck, K.; Chiang, Michael; Erdogmus, D.

IEEE International Workshop on Machine Learning for Signal Processing, MLSP. 2012. 6349809.

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

Ataer-Cansizoglu, E, You, S, Kalpathy-Cramer, J, Keck, K, Chiang, M & Erdogmus, D 2012, Observer and feature analysis on diagnosis of retinopathy of prematurity. in IEEE International Workshop on Machine Learning for Signal Processing, MLSP., 6349809, 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 M, Erdogmus D. Observer and feature analysis on diagnosis of retinopathy of prematurity. In IEEE International Workshop on Machine Learning for Signal Processing, MLSP. 2012. 6349809 https://doi.org/10.1109/MLSP.2012.6349809
Ataer-Cansizoglu, E. ; You, S. ; Kalpathy-Cramer, Jayashree ; Keck, K. ; Chiang, Michael ; Erdogmus, D. / Observer and feature analysis on diagnosis of retinopathy of prematurity. IEEE International Workshop on Machine Learning for Signal Processing, MLSP. 2012.
@inproceedings{8b892c2d4b594b3fa40f80f621aa7d0b,
title = "Observer and feature analysis on diagnosis of retinopathy of prematurity",
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.",
keywords = "feature selection, observer analysis, retinal image analysis",
author = "E. Ataer-Cansizoglu and S. You and Jayashree Kalpathy-Cramer and K. Keck and Michael Chiang and D. Erdogmus",
year = "2012",
doi = "10.1109/MLSP.2012.6349809",
language = "English (US)",
isbn = "9781467310260",
booktitle = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",

}

TY - GEN

T1 - Observer and feature analysis on diagnosis of retinopathy of prematurity

AU - Ataer-Cansizoglu, E.

AU - You, S.

AU - Kalpathy-Cramer, Jayashree

AU - Keck, K.

AU - Chiang, Michael

AU - Erdogmus, D.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - feature selection

KW - observer analysis

KW - retinal image analysis

UR - http://www.scopus.com/inward/record.url?scp=84870678072&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870678072&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2012.6349809

DO - 10.1109/MLSP.2012.6349809

M3 - Conference contribution

AN - SCOPUS:84870678072

SN - 9781467310260

BT - IEEE International Workshop on Machine Learning for Signal Processing, MLSP

ER -