tensorflow / tensorflow/datasets

[data request]KiTS19

Open
#989 1 comment 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

dataset request
Dominant language
Python
Stars
4.6k
Forks
1.6k
Avg merge
3h 54m
Merged PRs (30d)
1

Description

  • Name of dataset: 2019 Kidney and Kidney Tumor Segmentation (Kits19)
  • URL of dataset: https://kits19.grand-challenge.org/data/
  • License of dataset: MIT License
  • Short description of dataset and use case(s): This dataset is for 2019 Kidney and Kidney Tumor Segmentation Challenge. This dataset has CT scan images of patients who have Kidney Tumor. This dataset includes a training data of 210 patients and a test data of 90 patients. Automatic semantic segmentation of kidneys and kidney tumors is a promising tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task.
    For more information please refer to this paper: https://arxiv.org/abs/1904.00445

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

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with docs/add_dataset.md, then review the Kits19 dataset details at the linked challenge page and paper. Done means contributing Kits19 to tensorflow/datasets according to the repository guide.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, tensorflow
Domain
data, machine-learning
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.