This repository contains Python code to train and evaluate convolutional neural network models (CNNs) on the task of multiple abnormality prediction from whole chest CT volumes.
This repository includes code for numerous projects and models, including code related to the following research works:
Draelos, R. L., & Carin, L. "Explainable multiple abnormality classification of chest CT volumes." Artificial Intelligence in Medicine (2022).
- AxialNet
- HiResCAM
Draelos, R.L. "Towards fully automated interpretation of volumetric medical images with deep learning." Duke University PhD Thesis (2022).
Currently this repository represents the final state of my primary PhD codebase
after I defended and graduated. The runs directory includes "run files" that,
when moved to the root directory, can be run as
python runfile.py
I apologize that some of these run files assume an earlier state of the repo where classes/functions had slightly different interfaces. At some point I hope to "tutorialize" this repo so that it's straightforward to run everything of interest, but for now I figure sharing some code is better than sharing no code, so here you go :)
The requirements are specified in ct_environment.yml and include PyTorch, numpy, pandas, sklearn, scipy, and matplotlib.
To create the conda environment run:
conda env create -f ct_environment.yml
The code can also be run using the Singularity container defined in this repository.
This repo uses the Python unittest module for unit testing. You can use unittest discover to run the unit tests.
This research code was developed using the RAD-ChestCT dataset. The models in this codebase can be trained on the RAD-ChestCT dataset. CT scans from RAD-ChestCT are publicly available on Zenodo at this link.