# Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images

arXiv: https://arxiv.org/abs/2212.07721 · HTML: https://mlsanity.com/paper/2212.07721/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 1 of 1)

- [FCOS: A simple and strong anchor-free object detector](https://mlsanity.com/paper/2006.09214/) (2020)

## Built on by (most cited first, 1 of 1)

- [On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images](https://mlsanity.com/paper/2406.19899/) (2024)

## Abstract (arXiv)

Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be visualized using silver-based staining. The average number of AgNORs per nucleus has been shown to be a prognostic factor for predicting the outcome of many tumors. Since manual detection of AgNORs is laborious, automation is of high interest. We present a deep learning-based pipeline for automatically determining the AgNOR-score from histopathological sections. An additional annotation experiment was conducted with six pathologists to provide an independent performance evaluation of our approach. Across all raters and images, we found a mean squared error of 0.054 between the AgNOR- scores of the experts and those of the model, indicating that our approach offers performance comparable to humans.