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https://github.com/iliasprc/covidnet
PyTorch implementation of COVID-Net https://github.com/lindawangg/COVID-Net
https://github.com/iliasprc/covidnet
covid-19 python3 pytorch pytorch-implementation xray
Last synced: 2 months ago
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PyTorch implementation of COVID-Net https://github.com/lindawangg/COVID-Net
- Host: GitHub
- URL: https://github.com/iliasprc/covidnet
- Owner: iliasprc
- License: gpl-3.0
- Created: 2020-03-31T15:17:29.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-05-25T12:35:08.000Z (over 1 year ago)
- Last Synced: 2023-10-20T20:19:51.541Z (over 1 year ago)
- Topics: covid-19, python3, pytorch, pytorch-implementation, xray
- Language: Jupyter Notebook
- Size: 48.9 MB
- Stars: 79
- Watchers: 6
- Forks: 18
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# COVIDNet
[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
[](https://colab.research.google.com/github/IliasPap/COVIDNet/blob/master/COVIDNet.ipynb#scrollTo=G9t--VlR7_F6)
My PyTorch implementation of COVID-Net, for the original work please see: https://github.com/lindawangg/COVID-Net
The purpose of this github is to reproduce results and not to claim state-of-the-art performance !!
Also Google Colab Notebook for plug-n-play training and evaluation here [
## Table of Contents
* [Getting Started](#getting-started)
* [Installation](#installation)
* [Usage](#usage)
* [Results](#results)
* [Datasets](#datasets)
* [Contributing](#contributing)
* [License](#license)
* [Contact](#contact)
* [Acknowledgements](#acknowledgements)
## Getting Started
## TODOs
- [ ] Final Requirements
- [ ] Pretrained models
- [ ] Test all pretrained models
- [ ] Instructions for training
- [ ] Adding command line option for inference
## Requirements
### Installation & Data Preparation
Please refer to
```python
pip install -r requirements.txt
```
* Python >= 3.6 (3.6 recommended)
* PyTorch >= 1.4 (1.6.0 recommended)
* torchvision >=0.6.0
* tqdm (Optional for `test.py`)
* tensorboard >= 1.14
## Usage
### Training
The network takes as input an image of shape (N, 224, 224, 3) and outputs the softmax probabilities as (N, C), where N is the number of batches and C number of output classes.
1. To train the Network from scratch simply do `python main.py`
Arguments for training
```yaml
trainer:
cwd: /home/ # working directory
logger: CovidCLF # logger name
epochs: 30 # number of training epochs
seed: 123 # randomness seed
cuda: True # use nvidia gpu
gpu: 0,1 # id of gpu
save: True # save checkpoint
load: False # load pretrained checkpoint
gradient_accumulation: 1 # gradient accumulation steps
pretrained_cpkt: cpkt.pt
log_interval: 1000 # print statistics every log_interval
model:
name: mobilenet_v2 # model name [mobilenet_v2,COVIDNet_small]
optimizer: # optimizer configuration
type: SGD # optimizer type
lr: 1e-2 # learning rate
weight_decay: 0.000001 # weight decay
scheduler: # learning rate scheduler
type: ReduceLRonPlateau # type of scheduler
scheduler_factor: 0.5 # learning rate change ratio
scheduler_patience: 0 # patience for some epochs
scheduler_min_lr: 1e-3 # minimum learning rate value
scheduler_verbose: 5e-6 # print if learning rate is changed
dataloader:
train:
batch_size: 4 # batch size
shuffle: True # shuffle samples after every epoch
num_workers: 2 # number of thread for dataloader1
val:
batch_size: 2
shuffle: False
num_workers: 2
test:
batch_size: 1
shuffle: False
num_workers: 2
dataset:
input_data: ./data/data
name: COVIDx # dataset name COVIDx or COVID_CT
modality: RGB # type of modality
dim: [224,224] # image dimension
train:
augmentation: True # do augmentation to video
val:
augmentation: False
test:
augmentation: False
```
## Results
with my implementation of COVID-Net and comparison with CNNs pretrained on ImageNet dataset
### Results in COVIDx dataset
| Model | Accuracy (%) | # Params (M) | MACs (G) |
|:------------:|:------------:|:--------:|:-------------------:|
| [COVID-Net-Small] | | 89.10 | 115.42 | 2.26 |
| [COVID-Net-Large](https://drive.google.com/open?id=1-3SKFua_wFl2_aAQMIrj2FhowTX8B551) | 91.22 | 118.19 | 3.54 |
| [Mobilenet V2 ](https://drive.google.com/open?id=19J-1bW6wPl7Kmm0pNagehlM1zk9m37VV) | 94.0 | - | - |
| [ResNeXt50-32x4d](https://drive.google.com/open?id=1-BLolPNYMVWSY0Xnm8Y8wjQCapXiPnLx) | 95.0 | - | - |
| [ResNet-18](https://drive.google.com/open?id=1wxo4gkNGyrhR-1PG8Vr1hj65MfSAHOgJ) | 94.0 | - | - |
### Results in COVID-CT dataset
| Model | Accuracy (%) | # Params (M) | MACs (G) |
|:------------:|:------------:|:--------:|:-------------------:|
| [COVID-Net-Small] | - | - | |
| [COVID-Net-Large] | - | - | |
Confusion Matrix on both datasets coming soon !!
## Datasets
### 1) COVID-CT-Dataset
The COVID-CT-Dataset has 288 CT images containing clinical findings of COVID-19. We are continuously adding more COVID CTs.
The images are collected from medRxiv and bioRxiv papers about COVID-19. CTs containing COVID-19 abnormalities are selected by reading the figure captions in the papers. All copyrights of the data belong to medRxiv and bioRxiv.
Please refer to the preprint for details: COVID-CT-Dataset: A CT Scan Dataset about COVID-19
### 2) COVIDx dataset
The current COVIDx dataset is constructed by the following open source chest radiography datasets:
* https://github.com/ieee8023/covid-chestxray-dataset
* https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
We especially thank the Radiological Society of North America and others involved in the RSNA Pneumonia Detection Challenge, and Dr. Joseph Paul Cohen and the team at MILA involved in the COVID-19 image data collection project, for making data available to the global community.
### Steps to generate the COVIDx dataset
Download the datasets listed above
* `git clone https://github.com/ieee8023/covid-chestxray-dataset.git`
* go to this [link](https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data) to download the RSNA pneumonia dataset
2. Create a `data` directory and within the data directory, create a `train` and `test` directory
3. Use [COVIDNet.ipynb](COVIDNet.ipynb) to combine the two dataset to create COVIDx. Make sure to remember to change the file paths.
4. We provide the train and test txt files with patientId, image path and label (normal, pneumonia or COVID-19). The description for each file is explained below:
* [train\_COVIDx.txt](train_COVIDx.txt): This file contains the samples used for training.
* [test\_COVIDx.txt](test_COVIDx.txt): This file contains the samples used for testing.
[contributors-shield]: https://img.shields.io/github/contributors/iliasprc/COVIDNet.svg?style=flat-square
[contributors-url]: https://github.com/iliasprc/COVIDNet/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/iliasprc/COVIDNet.svg?style=flat-square
[forks-url]: https://github.com/iliasprc/COVIDNet/network/members
[stars-shield]: https://img.shields.io/github/stars/iliasprc/COVIDNet.svg?style=flat-square
[stars-url]: https://github.com/iliasprc/COVIDNet/stargazers
[issues-shield]: https://img.shields.io/github/issues/iliasprc/COVIDNet.svg?style=flat-square
[issues-url]: https://github.com/iliasprc/COVIDNet/issues
# Links
Check out this repository for more medical applications with deep-learning in PyTorch
https://github.com/black0017/MedicalZooPytorch from https://github.com/black0017