https://github.com/ayulockin/kagglerecipes
Helpful data preprocessing, training, and visualisation code and scripts for a range of Kaggle competitions, supported by Weights & Biases.
https://github.com/ayulockin/kagglerecipes
brain-tumor-classification classification kaggle kaggle-competition wandb
Last synced: 5 months ago
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Helpful data preprocessing, training, and visualisation code and scripts for a range of Kaggle competitions, supported by Weights & Biases.
- Host: GitHub
- URL: https://github.com/ayulockin/kagglerecipes
- Owner: ayulockin
- License: apache-2.0
- Created: 2021-08-09T04:29:21.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-10-11T12:41:40.000Z (almost 3 years ago)
- Last Synced: 2025-05-04T23:51:49.230Z (5 months ago)
- Topics: brain-tumor-classification, classification, kaggle, kaggle-competition, wandb
- Language: Jupyter Notebook
- Homepage: https://ayulockin.github.io/kagglerecipes/
- Size: 3.09 MB
- Stars: 15
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Kaggle Recipes
## What's New
#### Sept 3, 2021
- Bug fix
#### Sept 2, 2021
- Release version 0.0.3
- Added support for multiprocessing to extract DICOM metadata for RSNA-MICCAI Brain Tumor Classification competition.
- Bug fixes.
#### Aug 27, 2021
- Released the library on PyPI.
- Easily create voxel manipulated dataset for RSNA-MICCAI Brain Tumor Classification competition.
- Extract dicom metadata.
- Added utilities to log dataframe as tables and files/directory as artifacts.
- Added utiities to log basic W&B charts (line, bar, and scatter).
## Kaggle Competitions
- [RSNA-MICCAI Brain Tumor Radiogenomic Classification](https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification)
## Code & Scripts
#### RSNA-MICCAI Brain Tumor Radiogenomic Classification
- LINK TO NOTEBOOK
## Install
`pip install kagglerecipes`
## Sample Datasets
We have also logged smaller subsets of Kaggle commpeition datasets local development and fast prototyping.
#### RSNA-MICCAI Brain Tumor Radiogenomic Classification
* Download it manually from [here](https://wandb.ai/wandb_fc/rsna-miccai-brain/artifacts/dataset/sample/0c38392ee79fd5f85e97/files).
* Or download it using this code snippet.
```
import wandb
run = wandb.init()
artifact = run.use_artifact('wandb_fc/rsna-miccai-brain/sample:v0', type='dataset')
artifact_dir = artifact.download()
```
## Credits
The code in this repository is a combination of many of the Kagglers' work that they have shared publicly as Kaggle kernels.
* [Connecting voxel spaces](https://www.kaggle.com/boojum/connecting-voxel-spaces) by [Michael Beregov](https://www.kaggle.com/boojum)
* [Normalized Voxels: Align Planes and Crop](https://www.kaggle.com/ren4yu/normalized-voxels-align-planes-and-crop) by [yu4u](https://www.kaggle.com/ren4yu)
* [DICOM to 2D Resized Axial PNGs 256x256 [x36]](https://www.kaggle.com/smoschou55/dicom-to-2d-resized-axial-pngs-256x256-x36) by [Sofia Moschou](https://www.kaggle.com/smoschou55)
Note that this is not an official Weights and Biases product.