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--- |
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license: apache-2.0 |
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library_name: transformers |
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datasets: |
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- RationAI/PanNuke |
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tags: |
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- biology |
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--- |
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# LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images |
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Matěj Pekár, Vít Musil, Rudolf Nenutil, Petr Holub, Tomáš Brázdil |
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[[GitHub](https://github.com/RationAI/lsp-detr)] |
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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. |
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```python |
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from transformers import AutoModelForObjectDetection, AutoImageProcessor |
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processor = AutoImageProcessor.from_pretrained( |
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"RationAI/LSP-DETR", trust_remote_code=True |
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) |
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model = AutoModelForObjectDetection.from_pretrained( |
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"RationAI/LSP-DETR", trust_remote_code=True |
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) |
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inputs = processor(img, device=device, return_tensors="pt") |
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outputs = model(**inputs) |
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results = processor.post_process(outputs) |
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results = processor.post_process_instance( |
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results, height=img.size[1], width=img.size[0] |
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) |
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``` |
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## Citing LSP-DETR |
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```BibTeX |
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@misc{pekar2026lspdetr, |
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title={LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images}, |
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author={Matěj Pekár and Vít Musil and Rudolf Nenutil and Petr Holub and Tomáš Brázdil}, |
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year={2026}, |
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eprint={2601.03163}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2601.03163} |
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} |
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``` |
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