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Explaining counterfactual images

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Leveraging the expertise of physicians to identify medically meaningful features in ‘counterfactual’ images produced via generative machine learning facilitates the auditing of the inference process of medical-image classifiers, as shown for dermatology images.

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Fig. 1: Auditing ML models for the classification of dermatology images as ‘benign’ or ‘malignant’.

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Author information

Authors and Affiliations

  1. Google Research, Mountain View, CA, USA

    Oran Lang & Yun Liu

  2. Oregon Vision and Pharmacy LLC, Eugene, OR, USA

    Ilana Traynis

Authors
  1. Oran Lang
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  2. Ilana Traynis
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  3. Yun Liu
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Corresponding author

Correspondence to Yun Liu.

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Competing interests

O.L. and Y.L. are employees of Google and own Alphabet stock.

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Lang, O., Traynis, I. & Liu, Y. Explaining counterfactual images. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01164-5

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  • DOI: https://doi.org/10.1038/s41551-023-01164-5

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