Improve model card: Add pipeline tag, library name, project page, and citation

README.md

@@ -1,11 +1,13 @@
 ---
-license: cc-by-nc-sa-4.0
 datasets:
 - Jungle15/GDP-HMM_Challenge
 language:
 - en
+license: cc-by-nc-sa-4.0
 tags:
 - medical
+pipeline_tag: image-to-3d
+library_name: pytorch
 ---
 
 # Announcements
@@ -23,13 +25,11 @@ If you are not a reviewer, please note that downloading the dataset implies agre
 
 ## Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is a trained model can be directly used in GDP-HMM challenge in AAPM 2025. 
+This is a trained baseline model presented in the paper [Automating RT Planning at Scale: High Quality Data For AI Training](https://huggingface.co/papers/2501.11803). It can be directly used in the GDP-HMM challenge at AAPM 2025. 
 
 Please run this model and submit to the challenge platform, please follow the instructions in https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge. 
 
-*Attention*: This is a educational baseline that participants can quickly start the challenge and see some reasonable results. Participants may need to modify the settings and parameters to get better results.
+*Attention*: This is an educational baseline that participants can quickly start the challenge and see some reasonable results. Participants may need to modify the settings and parameters to get better results.
 
 - **Developed by:** Riqiang Gao
 - **Model type:** 3D CNN, U-Shaped, MedNeXt, PyTorch-Lightning
@@ -39,21 +39,28 @@ Please run this model and submit to the challenge platform, please follow the in
 
 ## Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Paper:** [Automating RT Planning at Scale: High Quality Data For AI Training](https://huggingface.co/papers/2501.11803)
+- **Project Page:** https://qtim-challenges.southcentralus.cloudapp.azure.com/competitions/38/
 - **Repository:** https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge.
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-
 Check the tutorials in the GitHub repository. 
 
+## Citation
 
+To acknowledge the work of challenge organization team and insights from previous publication, please kindly follow the instructions below. 
 
+- **Data citation**. If you find the data and challenge helpful for your research, please cite the following technique paper [1] that built the dataset (or/and the challenge summary paper when available). 
 
+- **Baseline citation**. If you find the method and code for data preprocessing and data loading in the repo (e.g., creating the angle and beam plates) inspiring for your work, please cite [2]. If you use or adjust MedNeXt as your network structure, please cite [3]. 
 
+In addition to above, if any data and code resources in this repo is helpful for your research, please kindly cite [1] or/and [2]. Please kindly cite external linked resources accordingly if they helped you. 
 
+```
+[1] Riqiang Gao, Mamadou Diallo, Han Liu, Anthony Magliari, Wilko Verbakel, Sandra Meyers, Masoud Zarepisheh, Rafe Mcbeth, Simon Arberet, Martin Kraus, Florin Ghesu, Ali Kamen. Automating RT Planning at Scale: High Quality Data For AI Training. arXiv preprint arXiv:2501.11803. 2025.
 
+[2] Riqiang Gao, Bin Lou, Zhoubing Xu, Dorin Comaniciu, and Ali Kamen. "Flexible-cm gan: Towards precise 3d dose prediction in radiotherapy." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
 
-
+[3] Saikat Ray, Gregor Koehler, Constantin Ulrich, Michael Baumgartner, Jens Petersen, Fabian Isensee, Paul F. Jaeger, and Klaus H. Maier-Hein. "Mednext: transformer-driven scaling of convnets for medical image segmentation." In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023.
+```