*Contrastive Learning for Generating Optical Coherence Tomography Images of the Retina
The repository is intended to provide supplementary materials and source code of the experimentes conducted in the paper. The paper is submitted SASHIMI2022 Workshop as part of MICCAI 2022 Conference.
The supplementary material can be found under reports folder. To acces it,click here.
Inspired by Cookie Cutter Data Science
├── LICENSE
├── README.md <- The top-level README for users of this project.
├── INSTALLATION.md <- Guidelines for users on how to install libraries/tools to conduct experiments.
├── DATAONBOARDING.md <- Information on how to download and use the data.
├── EXPERIMENTS.md <- Guidelines for users to conduct experiment reported in the paper.
├── Docker <- Dockerfile and bash scripts for building and running docker image for experiments.
├── data
│ ├── oct_test_all.csv <- The list of images in the test set with path info in csv format.
│ └── oct_train_all.csv <- The list of images in train set with path info in csv format.
│ └── oct_train_filtered.csv <- The list of images in filtered train set with path info in csv format.
│
├── models <- Trained and serialized models, model predictions, or model summaries.
│
├── notebooks <- Jupyter notebooks for exploratory data analysis and model training.
│
├── reports <- Generated supplementary materials.
│
│
├── src <- Source code for use in this project.
│ │
│ ├── dataloader <- Scripts to generate data for training.
│ │
│ ├── models <- Scripts to build model for training.
│ │
│ ├── utils <- Scripts for utility functions.
│ │
│ └── train_config.py <- Training configurations.
│
└── start_jupyter_notebook.sh <- Start jupyter notebook.
└── download_data.sh <- Download and extract OCT2017 data set.
└── requirements.txt <- Required python libs to install.
Please follow the insturction here to install development stack, dowload the data and conduct experiments.
- INSTALL.md: Follow the instructions in this file to install development stack.
- DATAONBOARDING.md: Follow the instructions in this file to download the data
- EXPERIMENTS.md: Follow the instructions in this file to conduct Exploratory data analysis(EDA) and train models.
We also provide the supplementary figures and algorithms mentioned in the repo. One may find more information regarding them under reports directory.
- Add a README file
- Add a CONTRIBUTING file
- Add a LICENSE