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Datasets:
Jungle15
/
GDP-HMM_Challenge

Tasks:
Image-to-Image
Languages:
English
Size:
1K<n<10K
Tags:
medical
radiotherapy
dose prediction
License:
Dataset card Files Community

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Dataset Card for GDP-HMM

This dataset is connected to the GDP-HMM challenge at AAPM 2025. The task is about generalizable dose prediction for radiotherapy.

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 first under "My Submissions" of the platform.

Dataset

In total, there are over 3500 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 test split will be full hidden to public. During the challenge, participants need to submit their solution via docker. After the challenge, 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
  • Shared by: Riqiang Gao
  • Language(s) (NLP): English
  • License: cc-by-nc-sa-4.0

Uses

The dataset is for research only. commercial use is not allowed.

Dataset Creation

Documented in the Reference [1].

Citation

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. Technique Note, 2025 (to be public soon).

[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.

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