Members: Eric He, Farris Atif, Nasser Al-Rayes, Zixiao Chen
Estimate intracranial pressure (ICP) given OCT scans and intraocular pressure (IOP) values. See our project poster.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ └── raw <- The original, immutable data dump.
│
├── docs <- Presentation information
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks for cleaning data and exploratory data analysis
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ ├── data <- Scripts to download or generate data
│ ├── models <- Scripts to train models and then use trained models to make
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Google Drive: holds raw data, reports
- Raw data: copy of the original monkey scans
- PyTorch reduction: PyTorch arrays of reduced monkey scans, NOT STANDARDIZED
- PyTorch standardized images: Pytorch tensors of reduced and standardized monkey scans (what we use for training)
- Image samples: Examples of images after downsizing (but not standardizing) - taken from the PyTorch reduction folder
- Master Dataset: holds master mappings from the raw data to our image samples, with IOP values filled in. Replicated to the repository
- Models: holds trained models from Google Colab
PyTorch reduction: code to downsize OCTs into PyTorch reduced arrays
From-scratch training: runs training using our forked 3-D resnet training code
Pre-trained: runs training using our forked MedicalNet model
Self-supervised: use the MoCo self-supervised learning to pre-train model