This is a segmentation model trained for pancretic and kidney lesion segmentation. It was trained with the Report Supervision (R-Super, MICCAI 2025, best paper award finalist) training methodology, which learns tumor segmentation directly from radiology reports (through new loss functions). This checkpoint was trained with 5K lesion reports from the UCSF dataset, plus 2K lesion masks from AbdomenAtlas 3.0 Beta. As far as we know, this is the public segmentation AI trained with the largest number of lesion CT scans (7K).
Training data: 16K CT scans
- 2,229 pancreatic tumor CT-Report pairs (UCSF)
- 2,738 kidney tumor CT-Report pairs (UCSF)
- 1,674 kidney tumor CT-Mask pairs (AbdomenAtlas 3.0)
- 344 pancreatic tumor CT-Mask pairs (AbdomenAtlas 3.0)
- 8,995 controls (CT scans without kidney or pancreas tumors)
Performance improvements are expected for models trained on the released version of AbdomenAtlas 3.0. For the ofifical release of AbdomenAtlas 3.0 (ICCV 2025), please check our GitHub: https://github.com/MrGiovanni/RadGPT. The AI model architecture is MedFormer, its training methology is Report Supervision (R-Super).
Training and inference code: https://github.com/MrGiovanni/R-Super
Label order
['adrenal_gland_left',
'adrenal_gland_right',
'aorta',
'bladder',
'celiac_trunk',
'colon',
'common_bile_duct',
'duodenum',
'esophagus',
'femur_left',
'femur_right',
'gall_bladder',
'hepatic_vessel',
'intestine',
'kidney_left',
'kidney_lesion',
'kidney_right',
'liver',
'liver_lesion',
'liver_segment_1',
'liver_segment_2',
'liver_segment_3',
'liver_segment_4',
'liver_segment_5',
'liver_segment_6',
'liver_segment_7',
'liver_segment_8',
'lung_left',
'lung_right',
'pancreas',
'pancreas_body',
'pancreas_head',
'pancreas_tail',
'pancreatic_lesion',
'portal_vein_and_splenic_vein',
'postcava',
'prostate',
'rectum',
'spleen',
'stomach',
'superior_mesenteric_artery',
'veins']
Papers
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
Finalist, Best Paper and Young Scientist Awards
RadGPT: Constructing 3D Image-Text Tumor Datasets
Pedro R. A. S. Bassi, Mehmet Yavuz, Kang Wang, Sezgin Er, Ibrahim E. Hamamci, Wenxuan Li, Xiaoxi Chen, Sergio Decherchi, Andrea Cavalli, Yang Yang, Alan Yuille, Zongwei Zhou*
Johns Hopkins University
ICCV, 2025
Citations
If you use this data, please cite the 3 paper 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{bassi2025radgpt,
title={Radgpt: Constructing 3d image-text tumor datasets},
author={Bassi, Pedro RAS and Yavuz, Mehmet Can and Wang, Kang and Chen, Xiaoxi and Li, Wenxuan and Decherchi, Sergio and Cavalli, Andrea and Yang, Yang and Yuille, Alan and Zhou, Zongwei},
journal={arXiv preprint arXiv:2501.04678},
year={2025}
}
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.