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MSBP-Net: Multi-scale binary pattern encoding network for cancer classification in pathology images

About

A multi-scale approach identifies and leverages the patterns of the multiple scales within a deep neural network. The patterns of the features across multiple scales are encoded as a binary pattern code and further converted to a decimal number, before embedded back to the classification model.
Link to J-BHI paper.

All the models in this project were evaluated on the following datasets:

Set Up Environment

conda env create -f environment.yml
conda activate msbp_net
pip install torch~=1.8.1+cu111

Above, we install PyTorch version 1.8.1 with CUDA 11.1. The code still work older Pytorch version (PyTorch >=1.1).

Repository Structure

Below are the main directories in the repository:

  • dataset/: the data loader and augmentation pipeline
  • docs/: figures/GIFs used in the repo
  • model/: model definition, along with the main run step and hyperparameter settings
  • prenet/: model definition, along with the main run step and hyperparameter settings
  • script/: defines the training/infer loop

Below are the main executable scripts in the repository:

  • config.py: configuration file
  • dataset.py: defines the dataset classes
  • define_network.py: defines the network
  • trainer.py: main training script
  • infer_produce_predict_map_wsi.py: following sliding window fashion to generate a predicted map for WSI images

Running the Code

Training and Options

  python trainer.py [--gpu=<id>] [--network_name=<network_name>] [--dataset=<colon/prostate>]

Options: ** Our proposed and other common/state-of-the-art multi-scale and single-scale methods, including:**

METHOD run_info Description
ResNet Resnet Feature extractor: ResNet50 (Code from Pytorch library)
VGG VGG Feature extractor: VGG16 (Code from Pytorch library)
MobileNetV1 MobileNetV1 Feature extractor: MobileNetV1 (Code from Pytorch library)
EfficientNet EfficientNet Feature extractor: EfficientNetB1 (Code from lukemelas) [Github]
ResNeSt ResNeSt Feature extractor: ResNeSt50 (Code from Pytorch library)
MuDeep MuDeep Multi_scale: Multi-scale deep learning architectures for person re-identification. [paper] [code]
MSDNet MSDNet Multi_scale: Multi-scale dense networks for resource efficient image classification. [paper] [code]
Res2Net Res2Net Multi_scale: Res2Net: A New Multi-scale Backbone Architecture [paper] [code]
FFN_concat ResNet_concat Multi_scale: Concat(multi_scale features)
FFN_add ResNet_add Multi_scale: Add(multi_scale features)
FFN_conv ResNet_conv Multi_scale: Conv(multi_scale features)
FFN_concat(z−µ) ResNet_concat_zm Multi_scale: Concat(multi_scale features - mean(multi_scale features))
FFN_conv(z−µ) ResNet_conv_zm Multi_scale: Conv(multi_scale features - mean(multi_scale features))
MSBP-Net ResNet_MSBP Multi_scale: Binary Pattern encoding layer (Ours)

Inference

  python infer_produce_predict_map_wsi.py [--gpu=<id>] [--network_name=<network_name>]

Model Weights

Model weights obtained from training MSBP here:

If any of the above checkpoints are used, please ensure to cite the corresponding paper.

Authors

  • Trinh, TL Vuong, Song, Boram and Kim, Kyungeun and Cho, Yong M. and Jin Tae Kwak

Citation

If any part of this code is used, please give appropriate citation to our paper.

BibTex entry: 

@ARTICLE{9496153,
  author={Vuong, Trinh T. L. and Song, Boram and Kim, Kyungeun and Cho, Yong M. and Kwak, Jin T.},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Multi-Scale Binary Pattern Encoding Network for Cancer Classification in Pathology Images}, 
  year={2022},
  volume={26},
  number={3},
  pages={1152-1163},
  doi={10.1109/JBHI.2021.3099817}}