Improve model card: Add metadata and paper links

README.md

@@ -1,8 +1,13 @@
-This is a segmentation model (MedFormer architecture) trained for pancreas tumor segmentation in the [PanTS](https://github.com/MrGiovanni/PanTS) public dataset. 
+---
+pipeline_tag: image-segmentation
+license: mit
+---
 
-This model was only trained with per-voxel segmentation masks. It serves as a public baseline for our MICCAI 2025 paper "Learning Segmentation from Radiology Report", the "segmentation" baseline. 
+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**](https://huggingface.co/papers/2510.14803).
 
-Also, this is the model is a starting point for our R-Super: you can can fine-tune it with radiology reports, please see our [Report Supervision (R-Super) GitHub](https://github.com/MrGiovanni/R-Super).
+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](https://github.com/MrGiovanni/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](https://github.com/MrGiovanni/R-Super).
 
 **Training and inference code: https://github.com/MrGiovanni/R-Super**
 
@@ -39,10 +44,14 @@ Also, this is the model is a starting point for our R-Super: you can can fine-tu
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-
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 # Papers
 
+<b>Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks</b> <br/>
+[Pedro R. A. S. Bassi](https://scholar.google.com/citations?user=NftgL6gAAAAJ&hl=en), [Wenxuan Li](https://scholar.google.com/citations?hl=en&user=tpNZM2YAAAAJ), [Jieneng Chen](https://scholar.google.com/citations?user=yLYj88sAAAAJ&hl=zh-CN), Zheren Zhu, Tianyu Lin, [Sergio Decherchi](https://scholar.google.com/citations?user=T09qQ1IAAAAJ&hl=it), [Andrea Cavalli](https://scholar.google.com/citations?user=4xTOvaMAAAAJ&hl=en), [Kang Wang](https://radiology.ucsf.edu/people/kang-wang), [Yang Yang](https://scholar.google.com/citations?hl=en&user=6XsJUBIAAAAJ), [Alan Yuille](https://www.cs.jhu.edu/~ayuille/), [Zongwei Zhou](https://www.zongweiz.com/)* <br/>
+*Johns Hopkins University* <br/>
+<a href='https://huggingface.co/papers/2510.14803'><img src='https://img.shields.io/badge/Paper-HuggingFace-blue'></a>
+
 <b>Learning Segmentation from Radiology Reports</b> <br/>
 [Pedro R. A. S. Bassi](https://scholar.google.com/citations?user=NftgL6gAAAAJ&hl=en), [Wenxuan Li](https://scholar.google.com/citations?hl=en&user=tpNZM2YAAAAJ), [Jieneng Chen](https://scholar.google.com/citations?user=yLYj88sAAAAJ&hl=zh-CN), Zheren Zhu, Tianyu Lin, [Sergio Decherchi](https://scholar.google.com/citations?user=T09qQ1IAAAAJ&hl=it), [Andrea Cavalli](https://scholar.google.com/citations?user=4xTOvaMAAAAJ&hl=en), [Kang Wang](https://radiology.ucsf.edu/people/kang-wang), [Yang Yang](https://scholar.google.com/citations?hl=en&user=6XsJUBIAAAAJ), [Alan Yuille](https://www.cs.jhu.edu/~ayuille/), [Zongwei Zhou](https://www.zongweiz.com/)* <br/>
 *Johns Hopkins University* <br/>
@@ -84,4 +93,4 @@ If you use this data, please cite the 2 papers below:
 
 ## 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](https://researchit.jhu.edu/) for their support and infrastructure resources where some of these analyses were conducted; especially [DISCOVERY HPC](https://researchit.jhu.edu/research-hpc/). Paper content is covered by patents pending.
+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](https://researchit.jhu.edu/) for their support and infrastructure resources where some of these analyses were conducted; especially [DISCOVERY HPC](https://researchit.jhu.edu/). Paper content is covered by patents pending.