Image Feature Extraction
timm
Safetensors
histopathology
pathology
dino
vision-transformer
prostate
feature-extraction
Instructions to use waticlems/Prost40M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use waticlems/Prost40M with timm:
import timm model = timm.create_model("hf_hub:waticlems/Prost40M", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -81,4 +81,14 @@ Apache-2.0
|
|
| 81 |
|
| 82 |
If you use **Prost40M**, cite:
|
| 83 |
|
| 84 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 81 |
|
| 82 |
If you use **Prost40M**, cite:
|
| 83 |
|
| 84 |
+
```
|
| 85 |
+
@misc{grisi2026bcr,
|
| 86 |
+
title={Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer},
|
| 87 |
+
author={Clément Grisi and Khrystyna Faryna and Nefise Uysal and Vittorio Agosti and Enrico Munari and Solène-Florence Kammerer-Jacquet and Paulo Guilherme de Oliveira Salles and Yuri Tolkach and Reinhard Büttner and Sofiya Semko and Maksym Pikul and Axel Heidenreich and Jeroen van der Laak and Geert Litjens},
|
| 88 |
+
year={2026},
|
| 89 |
+
eprint={2603.14187},
|
| 90 |
+
archivePrefix={arXiv},
|
| 91 |
+
primaryClass={cs.CV},
|
| 92 |
+
url={https://arxiv.org/abs/2603.14187},
|
| 93 |
+
}
|
| 94 |
+
```
|