Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
SYNTA demonstrates great potential to accelerate and improve biomedical image analysis. Its ability to generate high-quality photo-realistic synthetic data reduces reliance on extensive collection of data and manual annotations, paving the way for advancements in histopathology and medical research.
Authors
- Mónica Pascual
- Samir Jabari (ORCID: https://orcid.org/0000-0002-3037-7170)
- Jochen A. Ackermann (ORCID: https://orcid.org/0009-0009-1922-0306)
- Christoph S. Clemen (ORCID: https://orcid.org/0000-0002-1291-4219)
- Stefan Uderhardt (ORCID: https://orcid.org/0000-0002-2489-2109)
- Katrin Palumbo‐Zerr
- Gerhard Krönke (ORCID: https://orcid.org/0000-0002-7566-4325)
- Christian Holtzhausen
- Oliver Aust
- Leonid Mill (ORCID: https://orcid.org/0000-0001-6449-7309)
- Anika Grüneboom (ORCID: https://orcid.org/0000-0002-7928-5613)
- Georg Andreas Schett (ORCID: https://orcid.org/0000-0001-8740-9615)
- Andreas Maier (ORCID: https://orcid.org/0000-0002-9550-5284)
- Philipp Burger
- Rolf Schröder
Institutions
- World Vision (US)
- University of Cologne (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Universitätsklinikum Erlangen (DE)
- Nuremberg Hospital (DE)
- Leibniz Institute for Analytical Sciences - ISAS (DE)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1038/s43856-025-00777-y
- Citations
- 6
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 14.89