Benjaminzcq / UW-Madison-GI-Tract-Image-Segmentation

Track healthy organs in medical scans to improve cancer treatment

Semantic segmentation

Introduction

This repository contains source code generated by Luminide. It may be used to train, validate and tune deep learning models for image segmentation. The following directory structure is assumed:

├── code (source code)
├── input (dataset)
└── output (working directory)

The dataset should have images inside a directory named train and a CSV file named train.csv. An example is shown below:

input
├── train.csv
└── train
    ├── slice_0001_266_266_1.50_1.50.png
    ├── slice_0002_266_266_1.50_1.50.png
    ├── slice_0003_266_266_1.50_1.50.png

The CSV file is expected to have class labels under a column named class and mask annotations under segmentation as in the example below:

id,class,segmentation
case123_day20_slice_0065,large_bowel,
case123_day20_slice_0065,small_bowel,
case123_day20_slice_0065,stomach,28094 3 28358 7 28623 9 28889 9 29155 9 29421 9 29687 9 29953 9 30219 9 30484 10 30750 10 31016 10 31282 10 31548 10 31814 10 32081 9 32347 8 32614 6

The annotations are assumed to be run length encoded (RLE) masks.

To use this repo with Luminide

• Accept competition rules.
• Attach a Compute Server that has a GPU (e.g. gcp-t4).
• Configure your Kaggle API token on the Import Data tab.
• On the Import Data tab, choose Kaggle Competition Data and then enter uw-madison-gi-tract-image-segmentation.
• Train a model using the Run Experiment menu.

Kaggle submission

• Upload the code to Kaggle as a dataset by using the Run Experiment menu (select Custom > kaggle.sh).
• To create a submission, copy kaggle.ipynb to a new Kaggle notebook.
• Add the notebook output of https://www.kaggle.com/luminide/wheels3 as Data.
• Add your dataset at https://www.kaggle.com/<kaggle_username>/kagglecode1 as Data.
• Add the relevant competition dataset as Data.
• Save the notebook after turning off the Internet setting and turning on the GPU.
• Submit the results and wait for the notebook to finish.
• Check the leaderboard to see your score!

Additional features

• Use the Experiment Tracking menu to track experiments.
• To tune the hyperparameters, edit sweep.yaml as desired and launch a sweep from the Run Experiment tab. Tuned values will be copied back to a file called config-tuned.yaml along with visualizations in sweep-results.html.
• To use the tuned hyperparameter values, copy them over to config.yaml before training a model.
• For exploratory analysis, run eda.ipynb.
• To monitor training progress, use the Experiment Visualization menu.
• After an experiment is complete, use the file browser on the IDE interface to access the results on the IDE Server.
• To generate a report on the most recent training session, run report.sh from the Run Experiment tab. Make sure Track Experiment is checked. The results will be copied back to a file called report.html.

For more details on usage, see Luminide documentation