This is a segmentation model (MedFormer architecture) trained for pancreas tumor segmentation, as presented in the paper Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks.

It serves as a public baseline for the MICCAI 2025 paper "Learning Segmentation from Radiology Report," specifically referred to as the "segmentation" baseline, and is trained on the PanTS public dataset.

This model is also a starting point for our R-Super framework: you can fine-tune it with radiology reports. Please see our Report Supervision (R-Super) GitHub.

Training and inference code: https://github.com/MrGiovanni/R-Super

Label order

- adrenal_gland_left
- adrenal_gland_right
- aorta
- bladder
- colon
- common_bile_duct
- duodenum
- femur_left
- femur_right
- gall_bladder
- kidney_left
- kidney_right
- liver
- lung_left
- lung_right
- pancreas
- pancreas_body
- pancreas_head
- pancreas_tail
- pancreatic_lesion
- postcava
- prostate
- spleen
- stomach
- superior_mesenteric_artery
- veins

Papers

Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks
Pedro R. A. S. Bassi, Wenxuan Li, Jieneng Chen, Zheren Zhu, Tianyu Lin, Sergio Decherchi, Andrea Cavalli, Kang Wang, Yang Yang, Alan Yuille, Zongwei Zhou*
Johns Hopkins University

Learning Segmentation from Radiology Reports
Pedro R. A. S. Bassi, Wenxuan Li, Jieneng Chen, Zheren Zhu, Tianyu Lin, Sergio Decherchi, Andrea Cavalli, Kang Wang, Yang Yang, Alan Yuille, Zongwei Zhou*
Johns Hopkins University
MICCAI 2025
Best Paper Award Runner-up (top 2 in 1,027 papers)

Prize

PanTS: The Pancreatic Tumor Segmentation Dataset
Wenxuan Li, Xinze Zhou, Qi Chen, Tianyu Lin, Pedro R.A.S. Bassi, ..., Alan Yuille, Zongwei Zhou^★
Johns Hopkins University

Citations

If you use this data, please cite the 2 papers below:

@article{bassi2025learning,
  title={Learning Segmentation from Radiology Reports},
  author={Bassi, Pedro RAS and Li, Wenxuan and Chen, Jieneng and Zhu, Zheren and Lin, Tianyu and Decherchi, Sergio and Cavalli, Andrea and Wang, Kang and Yang, Yang and Yuille, Alan L and others},
  journal={arXiv preprint arXiv:2507.05582},
  year={2025}
}

@article{li2025pants,
  title={PanTS: The Pancreatic Tumor Segmentation Dataset},
  author={Li, Wenxuan and Zhou, Xinze and Chen, Qi and Lin, Tianyu and Bassi, Pedro RAS and Plotka, Szymon and Cwikla, Jaroslaw B and Chen, Xiaoxi and Ye, Chen and Zhu, Zheren and others},
  journal={arXiv preprint arXiv:2507.01291},
  year={2025},
  url={https://github.com/MrGiovanni/PanTS}
}

Acknowledgement

This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research, the Patrick J. McGovern Foundation Award, and the National Institutes of Health (NIH) under Award Number R01EB037669. We would like to thank the Johns Hopkins Research IT team in IT@JH for their support and infrastructure resources where some of these analyses were conducted; especially DISCOVERY HPC. Paper content is covered by patents pending.

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