Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features
Radiomic features processed through GNNs show significant promise for multi-disease classification, improving diagnostic accuracy. This study's findings suggest that integrating advanced imaging analysis with graph-based modeling can lead to better diagnostic tools. Future research should expand these methods to a wider range of diseases to validate their generalizability and effectiveness.
Authors
- Leonhard Rist (ORCID: https://orcid.org/0000-0002-9589-3774)
- Katharina Breininger (ORCID: https://orcid.org/0000-0001-7600-5869)
- Christian Ostalecki
- Julio Vera (ORCID: https://orcid.org/0000-0002-3076-5122)
- Luis Carlos Rivera Monroy (ORCID: https://orcid.org/0000-0001-8232-8920)
- Andreas Bauer (ORCID: https://orcid.org/0000-0002-7681-3177)
- Andreas Maier (ORCID: https://orcid.org/0000-0002-9550-5284)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University of Würzburg (DE)
- Universitätsklinikum Erlangen (DE)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1007/s11548-024-03277-x
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Friedrich-Alexander-Universität Erlangen-Nürnberg
- Deutsche Forschungsgemeinschaft