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.

• 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.

• 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!

• 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