https://github.com/auggen21/lits-challenge

Liver Tumor Segmentation Challenge
https://github.com/auggen21/lits-challenge

colab desktop-app download-lits-from-torrent image-classification image-processing image-segmentation ipynb kivy-application lits-challenge lits-data liver-tumor prediction

Last synced: about 1 month ago
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Liver Tumor Segmentation Challenge

• Host: GitHub
• URL: https://github.com/auggen21/lits-challenge
• Owner: Auggen21
• Created: 2020-04-30T17:24:22.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-05-13T18:45:44.000Z (over 4 years ago)
• Last Synced: 2024-10-11T15:41:00.040Z (about 1 month ago)
• Topics: colab, desktop-app, download-lits-from-torrent, image-classification, image-processing, image-segmentation, ipynb, kivy-application, lits-challenge, lits-data, liver-tumor, prediction
• Language: Jupyter Notebook
• Size: 6.3 MB
• Stars: 14
• Watchers: 1
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# LITS Challenge

 Liver Tumor Segmentation Challenge

1. Download the notebook and upload it in google colab.

2. Download the LITS data using the Download the LITS data.ipynb file, The dataset size is around 50 GB, so you may require paid storage for google drive as it supports only 15GB. Upload the torrent file when asked in colab.

3. It has around 100+ files, but you cannot train these much in colab

4. You can download a smaller dataset from this link which can be used  in colab training

https://drive.google.com/drive/folders/1_pzWz60wAvsMx35kd_x8w5A5Xyyf6ulh?usp=sharing

5. Train the model using model training.ipynb

6. Predict the new data using the model LITS_pred.ipynb

You can download the remaining dataset from here

https://drive.google.com/drive/folders/1-1rskjkiG46RkeoRpxzI4JHa1sBKxOQM?usp=sharing

aap-lits.py will create a desktop application using kivy which can be used for the prediction of liver tumor segmentation