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ENHANCE-PET / MOOSE

MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet and has the capability to segment 120 unique tissue classes from a whole-body 18F-FDG PET/CT image.
https://enhance.pet
GNU General Public License v3.0
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Can you share the training data? #66

Closed suyanzhou626 closed 1 year ago

suyanzhou626 commented 1 year ago

Hi, I am reaching out to inquire about the possibility of sharing the training data used for the MOOSE project on GitHub. I believe that access to this data would greatly benefit the development community by enabling us to train even better models and further enhance their accuracy.

The MOOSE project has garnered significant attention and has proven to be an invaluable resource for various tasks. I am impressed by the capabilities of the models developed by your team. However, I believe that by sharing the training data, the QIMP Team can unlock even greater potential for innovation and advancement in the field.

Having access to the training data would allow developers to experiment with different techniques, fine-tune models for specific use cases, and potentially discover new insights. This would not only enhance the accuracy of the existing models but also facilitate the creation of new models tailored to specific domains or languages.

I understand that there might be challenges associated with sharing training data, such as privacy concerns or intellectual property considerations. However, I believe that by implementing appropriate safeguards and anonymizing the data, these challenges can be overcome while still maintaining the integrity of the models.

In conclusion, I kindly request that the QIMP Team considers the possibility of sharing the training data for the MOOSE project on GitHub. Doing so would empower developers to train better models, leading to improved accuracy and ultimately benefiting the entire development community.

Thank you for considering this request. I appreciate your time and look forward to hearing your thoughts on this matter.

LalithShiyam commented 1 year ago

Hi @suyanzhou626 many thanks for the kind words and also for your suggestion on open-sourcing the data. It is a bit tricky as the data solely doesn't belong to our group, but from various data-sources (from different universities and different DTAs). We are pondering about this, but we haven't come to a concrete solution. We will keep you posted regarding your request.

LalithShiyam commented 1 year ago

Hi @suyanzhou626, unfortunately it is a no. Sorry...