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Interpretable Skin Cancer Classification based on Incremental Domain Knowledge Learning.

Eman Rezk1, Mohamed Eltorki2, Wael El-Dakhakhni1

  • 1School of Computational Science and Engineering, McMaster University, Hamilton, ON Canada.

Journal of Healthcare Informatics Research
|March 13, 2023
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Summary

This study introduces an interpretable artificial intelligence model for skin cancer diagnosis using clinical images. The AI achieves high accuracy in identifying lesion origin, malignancy, and disease type, aiding general practitioners in early detection.

Keywords:
Artificial intelligenceClinical imagesDomain knowledgeInterpretabilitySkin cancerSkin lesion taxonomy

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Area of Science:

  • Artificial Intelligence in Medicine
  • Dermatology
  • Medical Imaging Analysis

Background:

  • AI-driven diagnostic tools show promise but lack clinical trust due to their "black-box" nature.
  • Limited interpretability hinders the integration of AI in clinical workflows for skin cancer diagnosis.

Purpose of the Study:

  • To develop an interpretable AI model for skin cancer diagnosis using clinical images.
  • To enhance physician trust and facilitate the adoption of AI in dermatology.

Main Methods:

  • Developed an AI model integrating a skin lesion taxonomy for domain knowledge.
  • Incorporated a visualization method to highlight regions of interest in lesion images.
  • Trained and validated the model on easily obtainable clinical images.

Main Results:

  • Achieved 87% accuracy in predicting melanocytic vs. non-melanocytic lesions.
  • Reached 77% accuracy for malignancy prediction and 71% for disease diagnosis.
  • Interpretability methods aided in understanding model decisions and identifying misdiagnoses.

Conclusions:

  • The developed interpretable AI model effectively aids in skin cancer diagnosis using clinical images.
  • This approach can empower general practitioners with early diagnostic capabilities, reducing unnecessary specialist referrals.