Add task category, Hugging Face paper link, GitHub link, project page, and sample usage

README.md

@@ -1,14 +1,17 @@
 ---
-configs: 
-- config_name: meta_data
-  data_files: "GDP-HMM_train_valid_splits.json"
-license: cc-by-nc-sa-4.0
 language:
 - en
-tags:
-- medical
+license: cc-by-nc-sa-4.0
 size_categories:
 - 1K<n<10K
+pretty_name: AIRTP_Numpy
+configs:
+- config_name: meta_data
+  data_files: GDP-HMM_train_valid_splits.json
+tags:
+- medical
+task_categories:
+- image-to-3d
 extra_gated_fields:
   Full Real Name: text
   Institutional email: text
@@ -25,43 +28,38 @@ extra_gated_fields:
   I agree to register the GDP-HMM challenge if I use the data before the end of the challenge: checkbox
   I understand that my access to the data may be declined/removed if I have not correctly finished above steps: checkbox
   I agree to read the README file and follow citation terms before downloading data: checkbox
-pretty_name: AIRTP_Numpy
 ---
 
-# Announcements
+# GDP-HMM Dataset (AIRTP_Numpy)
 
-The paper for this dataset has been submitted for single-blind review. During the review period, access requests are temporarily disabled. 
-If you are not a reviewer, please note that downloading the dataset implies agreement with the following terms: 
+This dataset, presented in the paper [Automating RT Planning at Scale: High Quality Data For AI Training](https://huggingface.co/papers/2501.11803), is connected to the [GDP-HMM challenge](https://qtim-challenges.southcentralus.cloudapp.azure.com/competitions/38) at AAPM 2025. It provides high-quality data for AI training in radiotherapy planning, specifically for generalizable 3D dose prediction. The task involves generalizable dose prediction for radiotherapy.
 
-  * I agree to use this dataset for non-commercial use ONLY
-  
-  * I agree to register the GDP-HMM challenge if I use the data before the end of the challenge
-  
-  * I understand that my access to the data may be declined/removed if I have not correctly finished above steps
-  
-  * I agree to read the README file and follow citation terms before downloading data
+**Paper:** [Automating RT Planning at Scale: High Quality Data For AI Training](https://huggingface.co/papers/2501.11803)
+**Code:** [GitHub Repository](https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge)
+**Project Page:** [GDP-HMM Challenge Website](https://qtim-challenges.southcentralus.cloudapp.azure.com/competitions/38)
 
-# Dataset Card for GDP-HMM
+## Announcements
 
-<!-- Provide a quick summary of the dataset. -->
+The paper for this dataset has been submitted for single-blind review. During the review period, access requests are temporarily disabled.
+If you are not a reviewer, please note that downloading the dataset implies agreement with the following terms:
 
-This dataset is connected to the [GDP-HMM challenge](https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge) at AAPM 2025. The task is about generalizable dose prediction for radiotherapy. 
+  * I agree to use this dataset for non-commercial use ONLY
 
-The tutorials of using this dataset can be found in [GitHub](https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge). 
+  * I agree to register the GDP-HMM challenge if I use the data before the end of the challenge
 
-By downloading the dataset before the end of GDP-HMM challenge (May 2025), you are agreed to participate Phase I through III of the challenge and need to register the [challenge](https://qtim-challenges.southcentralus.cloudapp.azure.com/competitions/38) first under "My Submissions" of the platform. 
+  * I understand that my access to the data may be declined/removed if I have not correctly finished above steps
 
-This repo provides the data with Numpy format which can be directly used with our GitHub baseline. For raw DICOM format, please visit [Radiotherapy_HaN_Lung_AIRTP](https://huggingface.co/datasets/Jungle15/Radiotherapy_HaN_Lung_AIRTP). 
+  * I agree to read the README file and follow citation terms before downloading data
 
 ## Dataset
 
-In total, there are over 3700 RT plans included in the challenge covering head-and-neck and lung sites and IMRT & VMAT planning modes. There are three splits for the dataset. 
+In total, there are over 3700 RT plans included in the challenge covering head-and-neck and lung sites and IMRT & VMAT planning modes. There are three splits for the dataset.
 
 The training split includes both input and label. The input include CT image, PTVs, OARs, helper structures, beam geometries, prescribed dose, etc.
 
-The validation split only has the input shared to public. The participants of the challenge and researchers can submit their prediction to the challenge platform to get evalution results. We plan support this evaluation even during post-challenge and post the ranking in leaderboard. 
+The validation split only has the input shared to public. The participants of the challenge and researchers can submit their prediction to the challenge platform to get evalution results. We plan support this evaluation even during post-challenge and post the ranking in leaderboard.
 
-The test split will be full hidden to public. During the challenge, participants need to submit their solution via docker. After the challenge, we will release the reference plans of the validation set and researchers can contact the lead organizer for collaboration to test on the hidden split. 
+The test split will be full hidden to public. During the challenge, participants need to submit their solution via docker. After the challenge, we will release the reference plans of the validation set and researchers can contact the lead organizer for collaboration to test on the hidden split.
 
 - **Curated by:** Riqiang Gao and colleagues at Siemens Healthineers
 - **Funded by:** Siemens Healthineers
@@ -69,25 +67,27 @@ The test split will be full hidden to public. During the challenge, participants
 - **Language(s) (NLP):** English
 - **License:** cc-by-nc-sa-4.0
 
+## Sample Usage
 
-## Uses
+Detailed instructions for getting started with the dataset, data preprocessing, training a simple baseline model, and evaluation methods are provided in the Jupyter notebooks within the [GitHub repository](https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge).
 
-<!-- Address questions around how the dataset is intended to be used. -->
+Refer to the following specific notebooks for guidance:
+- [`get_started_and_train.ipynb`](https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge/blob/main/get_started_and_train.ipynb) for training and baseline code.
+- [`data_visual_understand.ipynb`](https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge/blob/main/data_visual_understand.ipynb) for data understanding and visualization.
+- [`evaluation.ipynb`](https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge/blob/main/evaluation.ipynb) for evaluation methods.
 
-The dataset is for research only. commercial use is not allowed.
+## Uses
 
+The dataset is for research only. Commercial use is not allowed.
 
 ## Dataset Creation
 
-Documented in the Reference [1]. We sincerely acknowledge the support of TCIA (https://www.cancerimagingarchive.net) for data release. 
-
+Documented in the Reference [1]. We sincerely acknowledge the support of TCIA (https://www.cancerimagingarchive.net) for data release.
 
 ## Citation
 
-If you use the dataset for your research, please cite below papers: 
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+If you use the dataset for your research, please cite below papers:
 
-[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 High Quality RT Planning at Scale. arXiv preprint arXiv:2501.11803. 2025.
+[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. (2025). [https://huggingface.co/papers/2501.11803](https://huggingface.co/papers/2501.11803).
 
 [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.