Publisher Correction: Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
The original version of this article contained typographical errors in the axis labels of Figure 2b, Figure 2e, and Figure 3b. The original article has been corrected.
Authors
- Markus Kreuz (ORCID: https://orcid.org/0000-0002-0520-8391)
- Hans‐Jonas Meyer (ORCID: https://orcid.org/0000-0001-8489-706X)
- H Goldschmidt
- Jonas Ader
- Adrian Lindenmeyer (ORCID: https://orcid.org/0000-0001-8977-5790)
- Nora Grieb (ORCID: https://orcid.org/0009-0003-4754-6887)
- Kristin Reiche (ORCID: https://orcid.org/0000-0002-4452-4872)
- Thomas Neumuth (ORCID: https://orcid.org/0000-0001-6999-5024)
- Thomas Wiemers
- Beatrice Berneck
- Uta Bertsch
- Maximilian Ferle
- Elias K. Mai
- Maximilian Merz
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- National Center for Tumor Diseases (DE)
- University Hospital Leipzig (DE)
- Fraunhofer Institute for Cell Therapy and Immunology (DE)
- Leipzig University (DE)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-06-16
- DOI
- https://doi.org/10.1038/s41746-026-02833-w
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00