Comparative analysis of data collection methods for individualized modeling of radiologists' visual similarity judgments in mammograms

Georgia Tourassi, Hong Jun Yoon, Songhua Xu, Garnetta Morin-Ducote, Kathy Hudson

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Rationale and Objectives: We conducted an observer study to investigate how the data collection method affects the efficacy of modeling individual radiologists' judgments regarding the perceptual similarity of breast masses on mammograms. Materials and Methods: Six observers of varying experience levels in breast imaging were recruited to assess the perceptual similarity ofmammographic masses. The observers' subjective judgments were collected using (i) a rating method, (ii) a preference method, and (iii)ahybrid method combining rating and ranking. Personalized user models were developed with the collected data to predict observers' opinions. The relative efficacy of each data collection method was assessed based on the classification accuracy of the resulting usermodels. Results: The average accuracy of the user models derived from data collected with the hybrid method was 55.5±1.5%. The models were significantly more accurate ( P < .0005) than those derived from the rating (45.3±3.5%) and the preference (40.8±5%) methods. Onaverage, the rating data collection method was significantly faster than the other two methods ( P < .0001). No time advantage was observed between the preference and the hybrid methods. Conclusions: A hybrid method combining rating and ranking is an intuitive and efficient way for collecting subjective similarity judgments to model human perceptual opinions with a higher accuracy than other, more commonly used data collection methods.

Original languageEnglish (US)
Pages (from-to)1371-1380
Number of pages10
JournalAcademic Radiology
Volume20
Issue number11
DOIs
StatePublished - Nov 2013

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Keywords

  • Breast imaging
  • Mammography
  • Observer variability
  • Perception
  • Visual similarity user modeling

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