Evaluating Medical Image Segmentation Models Using Augmentation
Overall, our framework offers a pipeline for evaluating segmentation performance without relying on manually labeled ground truth data, establishing a foundation for future advancements in automated medical image analysis.
Authors
- Gemma Roig (ORCID: https://orcid.org/0000-0002-6439-8076)
- Julia Dietz (ORCID: https://orcid.org/0000-0001-6990-3257)
- Matthias Neitzel
- Benedikt Wichtlhuber
- Sari Saba-Sadiya (ORCID: https://orcid.org/0009-0005-7482-3274)
- Andreas Bucher (ORCID: https://orcid.org/0000-0001-9194-5955)
- L.M. Keller
- M. El Sayed
Institutions
- Goethe University Frankfurt (DE)
- University Hospital Frankfurt (DE)
Publication Details
- Journal
- Tomography
- Published
- 2024-12-23
- DOI
- https://doi.org/10.3390/tomography10120150
- Citations
- 1
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.25