Official Repository of: "Unsupervised Pathology Detection: A Deep Dive Into the State of the Art"

arXiv preprint: https://arxiv.org/abs/2303.00609

This repository contains code to reproduce experiments from the paper.

Abstract

Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the need for large labeled datasets and are more generalizable than their supervised counterparts in detecting any kind of rare pathology. As the Unsupervised Anomaly Detection (UAD) literature continuously grows and new paradigms emerge, it is vital to continuously evaluate and benchmark new methods in a common framework, in order to reassess the state-of-the-art (SOTA) and identify promising research directions. To this end, we evaluate a diverse selection of cutting-edge UAD methods on multiple medical datasets, comparing them against the established SOTA in UAD for brain MRI. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets. Additionally, we show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms, further increasing their performance. Finally, we perform a series of experiments in order to gain further insights into some unique characteristics of selected models and datasets.

Citation

If you find our work useful for your research, please consider citing:

@misc{UPD_study,
  doi = {10.48550/ARXIV.2303.00609}, 
  url = {https://arxiv.org/abs/2303.00609},
  author = {Lagogiannis, Ioannis and Meissen, Felix and Kaissis, Georgios and Rueckert, Daniel}
  title = {Unsupervised Pathology Detection: A Deep Dive Into the State of the Art},
  publisher = {arXiv},
  year = {2023}
}

Usage

Download this repository by running

git clone https://github.com/iolag/UPD_study/

in your terminal.

Environment

Create and activate the Anaconda environment:

conda env create -f environment.yml
conda activate upd

Additionally, you need to install the repository as a package:

python3 -m pip install --editable .

To be able to use Weights & Biases for logging follow the instructions at https://docs.wandb.ai/quickstart.

Data

CheXpert

To download CheXpert you must first register at https://stanfordmlgroup.github.io/competitions/chexpert/. After you receive the subscription confirmation e-mail, download the downsampled version (~11G) and extract the CheXpert-v1.0-small folder in data/datasets/CXR. No other steps are required and all splits are provided.

DDR

To download and prepare the DDR dataset, run:

bash UPD_study/data/data_preprocessing/prepare_DDR.sh

MRI: CamCAN, ATLAS, BraTS

To download and preprocess ATLAS and BraTS, first download ROBEX from https://www.nitrc.org/projects/robex and extract it in data/data_preprocessing/ROBEX. Then run:

bash UPD_study/data/data_preprocessing/prepare_ATLAS.sh
bash UPD_study/data/data_preprocessing/prepare_BraTS.sh

For ATLAS you need to apply for the data at https://fcon_1000.projects.nitrc.org/indi/retro/atlas.htm and receive the encryption password. During the run of prepare_ATLAS.sh you will be prompted to input the password.

For BraTS, Kaggle's API will be used to download the data. To be able to interact with the API, follow the instructions at https://www.kaggle.com/docs/api.

To download the CamCAN data, you need to apply for it at https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/index.php. After you download them, put them in data/datasets/MRI/CamCAN and run:

python UPD_study/data/data_preprocessing/prepare_data.py --dataset CamCAN

Experiments

To generate the "Main Results" from Tables 1 and 3 over all three seeds run:

bash UPD_study/experiments/main.sh 

Alternatively, for a single seed run:

bash UPD_study/experiments/main_seed10.sh 

To generate the "Self-Supervised Pre-training" results from Tables 2 and 4 over all three seeds run:

bash UPD_study/experiments/pretrained.sh

Alternatively, for a single seed run:

bash UPD_study/experiments/pretrained_seed10.sh      

To generate the "Complexity Analysis" results from Table 5 run:

bash UPD_study/experiments/benchmarks.sh

To generate "The Effects of Limited Training Data" results from Fig. 3 run:

bash UPD_study/experiments/percentage.sh

This repository is a work in progress and in need of more cleanup and documentation. If you face any issues or have any suggestions do not hesitate to contact me or open an issue.