https://github.com/birkhofflee/dataset-figshare
Figshare brain tumor dataset for Tensorflow
https://github.com/birkhofflee/dataset-figshare
Last synced: 3 months ago
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Figshare brain tumor dataset for Tensorflow
- Host: GitHub
- URL: https://github.com/birkhofflee/dataset-figshare
- Owner: BirkhoffLee
- Created: 2024-05-03T11:54:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-03T12:27:10.000Z (about 1 year ago)
- Last Synced: 2025-01-19T10:22:42.448Z (5 months ago)
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Citation: CITATIONS.bib
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README
# Figshare Brain Tumor MRI Dataset
This brain tumor dataset containing 3064 T1-weighted contrast-inhanced images
from 233 patients with three kinds of brain tumor: meningioma (708 slices),
glioma (1426 slices), and pituitary tumor (930 slices). The images are 512x512 pixels.
Original dataset was published here: https://doi.org/10.6084/m9.figshare.1512427.v5
This dataset utilises [the converted version](https://www.kaggle.com/datasets/denizkavi1/brain-tumor/data) of the original dataset. I host the zip file on my own server to make it easier to download, and made a TFDS dataset from it, which is this repo.
Note that I have not yet implemented the test scripts; please contribute if you can.
## Original Description
> This brain tumor dataset containing 3064 T1-weighted contrast-inhanced images
> from 233 patients with three kinds of brain tumor: meningioma (708 slices),
> glioma (1426 slices), and pituitary tumor (930 slices). Due to the file size
> limit of repository, we split the whole dataset into 4 subsets, and achive
> them in 4 .zip files with each .zip file containing 766 slices.The 5-fold
> cross-validation indices are also provided.
>
> -----
> This data is organized in matlab data format (.mat file). Each file stores a struct
> containing the following fields for an image:
>
> cjdata.label: 1 for meningioma, 2 for glioma, 3 for pituitary tumor
> cjdata.PID: patient ID
> cjdata.image: image data
> cjdata.tumorBorder: a vector storing the coordinates of discrete points on tumor border.
> For example, [x1, y1, x2, y2,...] in which x1, y1 are planar coordinates on tumor border.
> It was generated by manually delineating the tumor border. So we can use it to generate
> binary image of tumor mask.
> cjdata.tumorMask: a binary image with 1s indicating tumor region
>
> -----
> This data was used in the following paper:
> 1. Cheng, Jun, et al. "Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation
> and Partition." PloS one 10.10 (2015).
> 2. Cheng, Jun, et al. "Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector
> Representation." PloS one 11.6 (2016). Matlab source codes are available on github
> https://github.com/chengjun583/brainTumorRetrieval
>
> -----
> Jun Cheng
> School of Biomedical Engineering
> Southern Medical University, Guangzhou, China
> Email: [email protected]
## Usage
For example, on Google Colab:
```
!wget https://github.com/BirkhoffLee/dataset-figshare/archive/refs/heads/main.zip
!unzip main.zip
!rm main.zip
!mv dataset-figshare-main brain_tumor_figshare
!tfds build brain_tumor_figshare
```
After it's built, you can use it as a normal TFDS dataset:
```python
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
ds, info = tfds.load('brain_tumor_figshare', split='all', with_info=True)
tfds.as_dataframe(ds.take(4), info) # This takes 4 data and plots them
```
## License
The original dataset is licensed under the CC BY 4.0 License. If you use this dataset, please cite the original authors.