Update README.md

README.md

@@ -10,7 +10,9 @@ tags:
 
 # LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images
 
-[GitHub](https://github.com/RationAI/lsp-detr)
+Matěj Pekár, Vít Musil, Rudolf Nenutil, Petr Holub, Tomáš Brázdil
+
+[[GitHub](https://github.com/RationAI/lsp-detr)][[arXiv](https://arxiv.org/abs/2601.03163)]
 
 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.
 
@@ -34,4 +36,18 @@ 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}
+}
+```