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Serial Dependence in Dermatological Judgments.

Zhihang Ren1,2, Xinyu Li2, Dana Pietralla3

  • 1Vision Science Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA.

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|May 27, 2023
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Summary
This summary is machine-generated.

Serial dependence, where recent images influence current perception, biases skin cancer diagnosis. This visual phenomenon affects dermatological judgments, potentially leading to diagnostic errors in medical imaging.

Keywords:
computer visionmedical image perceptionsemantic similarityserial dependenceskin cancer diagnosticsystematic error

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Area of Science:

  • Cognitive Psychology
  • Medical Imaging Perception
  • Computational Vision

Background:

  • Serial dependence is a common visual phenomenon where sequential images appear more similar, aiding perception in natural environments.
  • This perceptual smoothing can become maladaptive in artificial settings like medical image analysis, where stimuli are often randomly ordered.

Purpose of the Study:

  • To investigate the occurrence and characteristics of serial dependence in dermatological image perception.
  • To determine if serial dependence in lesion malignancy judgments is influenced by image similarity and temporal factors.

Main Methods:

  • Analysis of 758,139 skin cancer diagnostic records from an online application.
  • Quantification of semantic similarity between sequential dermatology images using a computer vision model and human raters.
  • Testing for serial dependence in perceptual discrimination judgments of lesion malignancy.

Main Results:

  • Significant serial dependence was observed in the perceptual discrimination of lesion malignancy.
  • The degree of serial dependence was correlated with the semantic similarity between sequential images.
  • Serial dependence effects demonstrated a decay over time.

Conclusions:

  • Serial dependence can introduce systematic bias into store-and-forward dermatology diagnostic judgments.
  • Understanding this perceptual bias is crucial for identifying potential sources of error in medical image perception tasks.
  • Findings suggest strategies to mitigate errors arising from serial dependence in clinical practice.