README.md

---
license: cc-by-sa-4.0
---

# Model Card for GrandQC Tissue Detection Model

Re-host of the tissue detection model from GrandQC, it's used to segment tissue regions and background.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:**
  - Institute of Pathology, University Hospital Cologne, 50937, Cologne, Germany
  - Danube Private University, 3500, Krems an der Donau, Austria
- **Model type:** UNet++ with EfficientNetB0 Encoder
- **Model Stats:**
  - Params(M):
  - Image Size: 512 x 512
  - Resolution: 10 mpp

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [GrandQC on Github](https://github.com/cpath-ukk/grandqc)
- **Paper [optional]:** [GrandQC: A comprehensive solution to quality control problem in digital pathology](https://www.nature.com/articles/s41467-024-54769-y)

## Uses

See TIAToolBox Documentation


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@article{Weng2024,
  author = {Weng, Zhilong and Seper, Alexander and Pryalukhin, Alexey and Mairinger, Fabian and Wickenhauser, Claudia and Bauer, Marcus and Glamann, Lennert and Bläker, Hendrik and Lingscheidt, Thomas and Hulla, Wolfgang and Jonigk, Danny and Schallenberg, Simon and Bychkov, Andrey and Fukuoka, Junya and Braun, Martin and Schömig-Markiefka, Birgid and Klein, Sebastian and Thiel, Andreas and Bozek, Katarzyna and Netto, George J. and Quaas, Alexander and Büttner, Reinhard and Tolkach, Yuri},
  title = {GrandQC: A comprehensive solution to quality control problem in digital pathology},
  journal = {Nature Communications},
  year = {2024},
  pages = {10685},
  doi = {10.1038/s41467-024-54769-y},
  url = {https://doi.org/10.1038/s41467-024-54769-y},
  issn = {2041-1723}
  }
```