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Documentation

The project is composed of the following modules, more details are below:

Main scripts for training and test models

All the experiments consist of train and test classification and segmentation architectures. You can use train.py and test.py, respectively.

TODO write a bit more (?)

Pytorch-lightning modules

Since this project is developed using the pytorch-lightning framework, two key concepts are Modules to declare a new model and DataModules to organize of our dataset. Both of them are declared in src/, specifically in src/models/ and src/dataset.py, respectively. More information are in the next sections.

Deep learning models

In this project the following segmentation models are implemented:

  • FCN
  • DeepLab

TODO put a mini description

Datasets and datamodules

TODO

Configuration handling

The configuration managed with Hydra. Every aspect of the configuration is located in config/ folder. The file containing all the configuration is config.yaml; edit it to change any configuration parameters.

Additional utility scripts

In the scripts/ folder, there are all independent files not involved in the pytorch-lightning workflow for data preparation and visualization.

Due to the possibility of errors in the dataset, such as missing images, run the check-dataset.py script to detect such errors. Returns the elements to be removed from the json file (this can be done manually or via a script).

python -m scripts.check-dataset --dataset data/coco_dataset.json

In this work, the dataset was annotated with more labels than necessary. Some are grouped under more general labels. To aggregate all the labels of the three diseases studied in this work, the following script is executed. In addition, we split the dataset with the holdout method.

python -m scripts.simplify-dataset --folder data
python -m scripts.split-dataset --folder data

You can use the dataset-stats.py script to print the class occurrences for each dataset.

python -m scripts.dataset-stats --dataset data/dataset.json # entire dataset
python -m scripts.dataset-stats --dataset data/train.json # training set
python -m scripts.dataset-stats --dataset data/test.json # test set