Semantic hyperspectral image synthesis for cross-modality knowledge transfer in surgical data science
As our method is capable of augmenting HSI datasets in a manner agnostic to the modality of the leveraged data, it could serve as a blueprint for addressing the data bottleneck encountered for novel imaging modalities.
Authors
- Leonardo Ayala (ORCID: https://orcid.org/0000-0002-3574-2085)
- Lena Maier‐Hein (ORCID: https://orcid.org/0000-0003-4910-9368)
- Berkin Özdemir (ORCID: https://orcid.org/0000-0001-8927-4830)
- Evangelia Christodoulou (ORCID: https://orcid.org/0000-0001-7900-5952)
- Silvia Seidlitz (ORCID: https://orcid.org/0000-0002-1122-4793)
- Marco Hübner
- Maike Rees (ORCID: https://orcid.org/0000-0002-7674-9144)
- Alexander Studier‐Fischer (ORCID: https://orcid.org/0000-0001-8682-9300)
- Jan Sellner (ORCID: https://orcid.org/0000-0003-4469-8343)
- Felix Nickel (ORCID: https://orcid.org/0000-0001-6066-8238)
- Ahmad Bin Qasim (ORCID: https://orcid.org/0000-0002-6502-8851)
- Viet Tran Ba
Institutions
- Universität Hamburg (DE)
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- National Center for Tumor Diseases (DE)
- University Medical Centre Mannheim (DE)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1007/s11548-025-03364-7
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- H2020 European Research Council