The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.
If you are using the code/model/data provided here in a publication, please consider citing:
@article{qiu2022miniseg,
title={MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation},
author={Qiu, Yu and Liu, Yun and Li, Shijie and Xu, Jing},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2022},
publisher={IEEE}
}
@inproceedings{qiu2021miniseg,
title={MiniSeg: An Extremely Minimum Network for Efficient {COVID}-19 Segmentation},
author={Qiu, Yu and Liu, Yun and Li, Shijie and Xu, Jing},
booktitle={AAAI Conference on Artificial Intelligence},
pages={4846--4854},
year={2021}
}
We adopt a 5-fold cross validation to evaluate the proposed MiniSeg. The precomputed segmentation maps of five folds on four datasets are provided in the SegMaps folder.
The pretrained models of five folds on four datasets are provided in the Pretrained folder.
We use Python 3.5, PyTorch 0.4.1, cuda 9.0, and numpy 1.17.3 to test the code. The train.py script is for training, and the test.py script is for testing.
Before running the code, you should first put the images, masks, and data lists into the datasets folder. For examples, for COVID-19-CT100 dataset, the images are put in the $ROOT_DIR/datasets/COVID-19-CT100/tr_im folder, the masks are put in the $ROOT_DIR/datasets/COVID-19-CT100/tr_mask folde, and the tranining/testing data lists are put in the folder of $ROOT_DIR/datasets/COVID-19-CT100/dataList.
For convenience, we provide our data on Google Drive and Baidu Yunpan (提取码：vavt). Please download it and unzip it into the $ROOT_DIR directory.
For example, we use the following command to test MiniSeg on the COVID-19-CT100 dataset:
python test.py --model_name MiniSeg --data_name CT100 --pretrained Pretrained/COVID-19-CT100/<MODEL_NAME> --savedir ./outputs
The generated segmentation maps of five folds will be outputted into the folder of $ROOT_DIR/outputs/CT100/MiniSeg/crossVal0~crossVal4, respectively.
python train.py --max_epochs 80 --batch_size 5 --lr 1e-3 --lr_mode poly --savedir ./results_MiniSeg_crossVal --model_name MiniSeg --data_name CT100