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Best-classifier feedback in diagnostic classification training.

Corey J Bohil1, Andrew J Wismer1, Troy A Schiebel1

  • 1Department of Psychology, University of Central Florida, United States.

Journal of Applied Research in Memory and Cognition
|December 13, 2016
PubMed
Summary
This summary is machine-generated.

Learning to classify medical images is improved by using feedback from a "best" classifier, rather than "objective" feedback. This approach leads to better decision-making and higher rewards in diagnostic training.

Keywords:
ClassificationDiagnosisFeedbackOptimal-classifierTraining

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Area of Science:

  • Decision-making and classification learning
  • Machine learning in medical diagnostics
  • Cognitive psychology of learning

Background:

  • Effective diagnostic classification training relies on extensive examples and category membership feedback.
  • Objective feedback, while seemingly accurate, can be misleading when dealing with confusable categories or complex real-world data.
  • Prior research indicates that feedback from an 'optimal' (error-prone) responder enhances long-term rewards, particularly in scenarios with unequal category payoffs.

Purpose of the Study:

  • To investigate the impact of "best" classifier feedback versus "objective" feedback on diagnostic classification learning in a medical context.
  • To determine if using an empirically derived "best" classifier, rather than a theoretically optimal one, improves learning outcomes for complex stimuli.
  • To evaluate the effect of different feedback types on decision criteria, reward accumulation, and accuracy in mammography image classification.

Main Methods:

  • Participants were trained to classify mammography images as normal or cancerous.
  • Two feedback conditions were employed: "objective" feedback and feedback based on an empirically determined "best" classifier.
  • Performance was measured by decision-criterion values (β), total points earned (reward), and overall accuracy, with higher points awarded for correct cancer classifications.

Main Results:

  • Feedback based on the "best" classifier resulted in decision-criterion values closer to the theoretically reward-maximizing criterion.
  • Participants in the "best" classifier group achieved higher total point accumulation.
  • A slight, predicted reduction in overall accuracy was observed with the "best" classifier feedback, suggesting a trade-off between accuracy and reward optimization.

Conclusions:

  • Empirically derived "best" classifier feedback is more effective for diagnostic learning with complex, real-world stimuli than objective feedback.
  • This feedback strategy optimizes decision-making towards maximizing rewards, even if it slightly decreases overall classification accuracy.
  • The findings support the use of "best" classifier feedback in training systems for tasks like mammography interpretation where optimal responses are not easily defined.