Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
Official Code Repository Going alongside the Paper
Please cite the following work if you find this model useful for your research:
Silvia D. Almeida, Carsten T. Lüth, Tobias Norajitra, Tassilo Wald, Marco Nolden, Paul F. Jaeger, Claus P. Heussel, Jürgen Biederer, Oliver Weinheimer, Klaus Maier-Hein (2023).
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations. arXiv preprint arXiv:2307.07254
Official PyTorch implementation for paper cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations , accepted in MICCAI 2023.
Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being the third leading cause of death. Its sparse, diffuse and heterogeneous appearance on computed tomography challenges supervised binary classification. We reformulate COPD binary classification as an anomaly detection task, proposing cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we learn representations of unlabeled lung regions employing a self-supervised contrastive pretext model, potentially capturing specific characteristics of diseased and healthy unlabeled regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. Patient-level scores are obtained by aggregating region OOD scores. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared to the previous supervised state-of-the-art. Additionally, cOOpD yields well-interpretable spatial anomaly maps and patient-level scores which we show to be of additional value in identifying individuals in the early stage of progression. Experiments in artificially designed real-world prevalence settings further support that anomaly detection is a powerful way of tackling COPD classification.
- Python - version 3.7+
- See environment.yml
- FrEIA Framework
Contrastive Pretext Training on Medical Data
Set this up with conda:
$ conda env create -f environment.yml
Follow the instructions here
Set all the paths to the data and logs in:
- config/global_config
- config/datasets/lung.py
- create own datamodule carrying your data
- see --> datamodules/lung_module.py & datamodules/lung.py
Verify that everything is working
$ python train_pretext.py --fast_dev_run True
See in the logs folder, whether a log has been created, access with tensorboard via:
$ tensorboard --path {path_to_logs}
Fitting of the generative models
$ python train_latent_gen.py -p {path_to_experiment}
Evaluation of the generative models
$ python eval.py -p {path_to_experiment}
To run experiments with new datasets:
- Add a new datamodule
- Add a new option for
get_data_loaders(name_exp)ineval.py
To do this, add a new entry to the dictionary in the file config/latent_model.py.
Anomaly Detection with the cOOpD framework on 3D data.
Project is in progress
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization [link]
Copyright © German Cancer Research Center (DKFZ), Division of Medical Image Computing (MIC). Please make sure that your usage of this code is in compliance with the code license.