Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Machine learning

Explaining counterfactual images

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

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

References

  1. Gordon, R. Semin. Oncol. Nurs. 29, 160–169 (2013).

    Article  PubMed  Google Scholar 

  2. Zhang, X., Lin, D., Pforsich, H. & Lin, V. W. Hum. Resour. Health 18, 8 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lorkowski, J. & Jugowicz, A. Adv. Exp. Med. Biol. 1324, 57–62 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Tsang, M. W. & Resneck, J. S. Jr J. Am. Acad. Dermatol. 55, 54–58 (2006).

    Article  PubMed  Google Scholar 

  5. Colin, J., Fel, T., Cadène, R. & Serre, T. Adv. Neural Inf. Process. Syst. 35, 2832–2845 (2022).

    PubMed  PubMed Central  Google Scholar 

  6. Adebayo, J. et al. Adv. Neural Inf. Process. Syst. 32, 9525–9536 (2018).

    Google Scholar 

  7. DeGrave, A. J., Janizek, J. D. & Lee, S.-I. Nat. Mach. Intell. 3, 610–619 (2021).

    Article  Google Scholar 

  8. DeGrave, A. J., Cai, Z. R., Janizek, J. D., Daneshjou, R. & Lee, S.-I. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-023-01160-9 (2023).

    Article  Google Scholar 

  9. Wiens, J. et al. Nat. Med. 25, 1337–1340 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Lang, O. et al. Preprint at https://arxiv.org/abs/2306.00985 (2023).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yun Liu.

Ethics declarations

Competing interests

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

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

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics