We propose Space-aware memory QUeues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID). Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, SQUID consists of a new Memory Queue and a novel in-painting block in the feature space.
SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, SQUID can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses the state of the art in unsupervised anomaly detection by over 5 points on two chest X-ray benchmark datasets.
This repository provides the official Pytorch implementation of SQUID in the following papers:
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Tiange Xiang1, Yixiao Zhang2, Yongyi Lu2, Alan L. Yuille2, Chaoyi Zhang1, Weidong Cai1, and Zongwei Zhou2
1University of Sydney, 2Johns Hopkins University
CVPR, 2023
paper | code
★ An improved version can be found at SimSID 
Please clone our environment using the following command:
conda env create -f environment.yml
conda activate squid
checkpoints/: experiment folders, organized by unique exp identifier.dataloader/: dataloaders for zhanglab, chexpert, and digitanotamy.models/: models for SQUID, inpainting block, all kinds of memory, basic modules, and discriminator.configs/: configure files for different experiments, based on the base configure class.
ZhangLab Chest X-ray
Please download the offical training/testing and our validation splits from google drive, and unzip it to anywhere you like.
Stanford ChexPert
Please download the offical training/validation and our testing splits from google drive, and unzip it to anywhere you like.
DigitAnatomy
Please unzip the files in data/digitanatomy.zip, and place them to the data root as specified in the configs/base.py.
Different experiments are controlled by configure files, which are in configs/.
All configure files are inherited from the base configure file: configs/base.py, we suggest you to take a look at this base file first, and change the dataset root path in your machine.
Then, you can inherite the base configure class and change settings as you want.
We provide our default configures for ZhangLab: configs/zhang_best.py, CheXpert: configs/chexpert_best.py and configs/digit_best.py.
The path to a configure file needs to be passed to the program for training.
Train with a configure file and a unique experiment identifier:
python3 main.py --exp zhang_exp1 --config zhang_best
Alternatively, you can modify run.sh and simply run:
./run.sh
Evaluate with an exp identifier (config file will be imported from the exp folder instead):
python3 eval.py --exp zhang_exp1
Alternatively, you can modify eval.sh and simply run:
./eval.sh
If you use this work for your research, please cite our paper:
@article{xiang2023painting,
title={In-painting Radiography Images for Unsupervised Anomaly Detection},
author={Xiang, Tiange and Liu, Yongyi and Yuille, Alan L and Zhang, Chaoyi and Cai, Weidong and Zhou, Zongwei},
journal={IEEE/CVF Converence on Computer Vision and Pattern Recognition},
year={2023}
}
@article{xiang2024exploiting,
title={Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images},
author={Xiang, Tiange and Zhang, Yixiao and Lu, Yongyi and Yuille, Alan and Zhang, Chaoyi and Cai, Weidong and Zhou, Zongwei},
journal={arXiv preprint arXiv:2403.08689},
year={2024}
}
This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research. We appreciate the constructive suggestions from Yingda Xia, Jessica Han, Yingwei Li, Bowen Li, Adam Kortylewski, Huiyu Wang, and Sonomi Oyagi.