File size: 1,532 Bytes
c0b5137 10e2f7e c3c3886 c0b5137 c3c3886 b28dc4a c24a3b9 c9ad936 c3c3886 135e030 835482b c0b5137 835482b c0b5137 835482b b28dc4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
---
license: apache-2.0
library_name: transformers
datasets:
- RationAI/PanNuke
tags:
- biology
---
# LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images
Matěj Pekár, Vít Musil, Rudolf Nenutil, Petr Holub, Tomáš Brázdil
[[GitHub](https://github.com/RationAI/lsp-detr)]
LSP-DETR (Local Star Polygon DEtection TRansformer) is a lightweight, efficient, and end-to-end deep learning model for nuclei instance segmentation in histopathological images. It combines a DETR-based transformer decoder with star-convex polygon shape descriptors to enable accurate and fast segmentation without complex post-processing.
```python
from transformers import AutoModelForObjectDetection, AutoImageProcessor
processor = AutoImageProcessor.from_pretrained(
"RationAI/LSP-DETR", trust_remote_code=True
)
model = AutoModelForObjectDetection.from_pretrained(
"RationAI/LSP-DETR", trust_remote_code=True
)
inputs = processor(img, device=device, return_tensors="pt")
outputs = model(**inputs)
results = processor.post_process(outputs)
results = processor.post_process_instance(
results, height=img.size[1], width=img.size[0]
)
```
## Citing LSP-DETR
```BibTeX
@misc{pekar2026lspdetr,
title={LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images},
author={Matěj Pekár and Vít Musil and Rudolf Nenutil and Petr Holub and Tomáš Brázdil},
year={2026},
eprint={2601.03163},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.03163}
}
```
|