Automated lesion detection in endoscopic imagery for small animal models – a pilot study
The proposed approach reliably detects and classifies colon tumors in mice, offering real-time support for preclinical endoscopic studies. Further evaluation will provide more insights into its performance.
Authors
- Thomas C. Wittenberg (ORCID: https://orcid.org/0000-0003-0840-8695)
- Elisabeth Naschberger (ORCID: https://orcid.org/0000-0003-1291-622X)
- Bisan Abdalfatah Zohud
- Ralf Hackner
- Thomas Eixelberger (ORCID: https://orcid.org/0000-0002-9160-5093)
- Michael Stürzl (ORCID: https://orcid.org/0000-0002-9276-2824)
- Fang Qi (ORCID: https://orcid.org/0000-0003-2227-9340)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Universitätsklinikum Erlangen (DE)
- Fraunhofer Institute for Integrated Circuits (DE)
Publication Details
- Journal
- Biomedizinische Technik/Biomedical Engineering
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1515/bmt-2025-0179
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00