Image Segmentation
Transformers
PyTorch
repcontur
feature-extraction
medical
radiology
ct
3d
tumor-segmentation
referring-segmentation
report-generation
visual-question-answering
custom_code
Instructions to use Oncosoft-Research/RepConTUR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oncosoft-Research/RepConTUR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Oncosoft-Research/RepConTUR", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Oncosoft-Research/RepConTUR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Link arXiv paper 2610.40094
Browse files
README.md
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- **Code:** <https://github.com/Oncosoft-Research/RepConTUR>
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- **Data:** <https://huggingface.co/datasets/Oncosoft-Research/RepConTUR>
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- **Paper:**
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## Model
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@article{lee2026repcontur,
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title = {RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation},
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author = {Lee, Seunghwan and Park, Sangjoon and Lee, Jongmin and Kim, Yoon Jo and Cho, Wonyoung and Kim, Jin Sung and Chae, Han Joo},
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year = {2026}
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}
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```
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- **Code:** <https://github.com/Oncosoft-Research/RepConTUR>
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- **Data:** <https://huggingface.co/datasets/Oncosoft-Research/RepConTUR>
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- **Paper:** <https://arxiv.org/abs/2610.40094>
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## Model
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@article{lee2026repcontur,
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title = {RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation},
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author = {Lee, Seunghwan and Park, Sangjoon and Lee, Jongmin and Kim, Yoon Jo and Cho, Wonyoung and Kim, Jin Sung and Chae, Han Joo},
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journal = {arXiv preprint arXiv:2610.40094},
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year = {2026}
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}
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```
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