tensorflow / tensorflow/datasets
[data request] Pancreas-CT Scans
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- Python
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Description
- Name of dataset: Pancreas-CT Scans
- URL of dataset: https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT#82af2dca8f2443b1bef1e85ac73acd44
- License of dataset: Creative Commons Attribution 3.0 Unported License
- Short description of dataset and use case(s): This dataset contains 82 abdominal contrast enhanced 3D CT scans from 53 male and 27 female subjects. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 − 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage). A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified/modified by an experienced radiologist. This dataset could prove useful to develop algorithms to automatically segment the pancreas, and may lead to future research to detect pancreatic abnormalities.
Folks who would also like to see this dataset in tensorflow/datasets, please thumbs-up so the developers can know which requests to prioritize.
And if you'd like to contribute the dataset (thank you!), see our guide to adding a dataset.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Read docs/add_dataset.md first, then use the Pancreas-CT dataset URL and license details in the issue to understand the requested contribution. Done means the dataset is added to tensorflow/datasets with its metadata, access information, and segmentation use case represented; the issue does not name specific implementation files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100