Official Repository

"Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN"

The research article is available in Photodiagnosis and Photodynamic Therapy Journal, Volume 37, and can be accessed using this Link

List of Authors

Muhammad Mohsin Zafar
Zunaira Rauf
Anabia Sohail
Abdul Rehman Khan
Muhammad Obaidullah
Saddam Hussain Khan
Yeon Soo Lee
Asifullah Khan

Code Contributor

Muhammad Mohsin Zafar

Overview

This GitHub repository is released along with our submission "Detection of Lymphocytes in Histopathological Images using a Two-Phase Deep CNN" in Photodiagnosis and Photodynamic Therapy Journal. The repository at the moment contains only the inference code. Later on after the acceptance of our article for publication we intend on releasing complete code that shall include code for multiple experiments and training the models on same parameters as we did.

Requirements

Following are the prerequisites for using this repository.

Language

Python >= 3.7

Modules

All modules that were used, are mentioned in requirements.txt file.

gdown==3.13.0
opencv-python~=4.5.1.48
numpy~=1.19.2
matplotlib==3.3.4
tqdm~=4.59.0
pillow~=8.1.2
scikit-image==0.17.2
pytorch==1.8.0
torchvision==0.9.0
detectron2==0.5

Repository Setup

Please follow the instructions in order to use this repository effectively

Clone the Repository

git clone https://github.com/m-mohsin-zafar/tdc-lc

Install PyTorch

For installing PyTorch you can visit their website here OR

Conda Installation

OSX

# conda
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 -c pytorch

Linux and Windows

# CUDA 10.2
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch

# CUDA 11.1
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

# CPU Only
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cpuonly -c pytorch

pip wheels

OSX

pip install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0

Linux and Windows

# RocM 4.0.1 (Linux only)
pip install torch -f https://download.pytorch.org/whl/rocm4.0.1/torch_stable.html
pip install ninja
pip install 'git+https://github.com/pytorch/[email protected]'

# CUDA 11.1
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

# CUDA 10.2
pip install torch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0

# CPU only
pip install torch==1.8.0+cpu torchvision==0.9.0+cpu torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

Install Detectron2

Official Documentation for installing the Detectron2 API can be found here.
One method for installation of Detectron2 is as follows:

python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
# (add --user if you don't have permission)

# Or, to install it from a local clone:
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

# On macOS, you may need to prepend the above commands with a few environment variables:
CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ...

Install from requirements.txt

Finally, install rest of the modules from requirements.txt

pip

pip install -r requirements.txt

conda

conda install --file requirements.txt

Repository Structure

For a better understanding of repository please follow along with the explanation below.

    .
    ├── checkpoints               # Contains all the weight files of model
    │   ├── download.py
    │   └── README.md
    ├── data                      # Input image patches that you want to perform inference on
    │   ├── test_10080.png
    │   ├── test_10227.png
    ├── main.py                   # Entry Point file, you just need to run this
    ├── models                    # Code for building the models is placed here
    │   ├── build.py
    │   ├── configs.py
    │   └── __init__.py
    ├── output                    # Output of inference can be found here
    │   ├── result_count.csv      # The file contains count of lymphocytes found against each input image
    │   └── viz                   # Ouput visuals will be save in this folder
    ├── pipeline                  # Code for complete inference pipeline
    │   ├── inference.py
    │   ├── __init__.py
    │   ├── postprocess.py
    │   ├── preprocess.py
    │   └── tmp                   # Temporary DAB images that are saved during inference
    ├── README.md
    └── requirements.txt

Usage

Following are the steps to run inference.

1. Download Weights

Assuming, you are already inside the repository folder.

# Change pwd to ./checkpoints
cd checkpoints
# Run download.py file
python download.py

After running this, you'll see that two *.pth files are downloaded in checkpoints folder

2. Setting up Input Data

Place your data of Lymphocyte Image Patches in data folder. Preferably, the input images should be *.png format.
OR
You may use the already placed images in the folder just for testing purposes. Few sample images are shown below.

3. Run Inference

Assuming, you are in the root repository folder. You just need to execute main.py file.

python main.py

4. Check Output

The output is saved in the output folder. Inside the folder you will find a csv file named, "result_count.csv" which contains two columns as per following pattern.

file_name	count
test_15907.png	0
test_1949.png	3
test_12565.png	12

Also, in the output folder you'll find another folder named "viz" which shall contain the output visualizations. You may find some output visualizations to be plain black images, this would occur in case there are no predicted lymphocytes i.e. count=0. Some sample output visualizations are shown below.

Acknowledgements

We are thankful to Pakistan Institute of Engineering and Applied Sciences, Islamabad (PIEAS) and Pattern Recognition Lab for their kind support and facilitation in computational resources.

Citation

If you use the code in your research or wish to refer to the baseline results published in the article, please use the following BibTeX entry.

@article{ZAFAR2022102676,
    title = {Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN},
    journal = {Photodiagnosis and Photodynamic Therapy},
    volume = {37},
    pages = {102676},
    year = {2022},
    issn = {1572-1000},
    doi = {https://doi.org/10.1016/j.pdpdt.2021.102676},
    url = {https://www.sciencedirect.com/science/article/pii/S1572100021004932},
    author = {Muhammad Mohsin Zafar and Zunaira Rauf and Anabia Sohail and Abdul Rehman Khan and Muhammad Obaidullah and Saddam Hussain Khan and Yeon Soo Lee and Asifullah Khan},
    keywords = {tumor-infiltrating Lymphocytes (TILs), Mask R-CNN, Histopathological images, Lymphocyte detection, Deep Convolutional Neural Network (DCNN)}
}```

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