Skip to content

README.md

Latest commit

 

History

History
60 lines (49 loc) · 1.77 KB

README.md

File metadata and controls

60 lines (49 loc) · 1.77 KB

IDC_Grading_PyTorch

This repo contains the source code for Automated IDC Grading System in Pytorch using the FBCG dataset

Citation

If you find our code useful, please consider citing our work using the bibtex:

@article{
voon2022performance,
title={Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images},
author={Voon, Wingates and Hum, Yan Chai and Tee, Yee Kai and Yap, Wun-She and Salim, Maheza Irna Mohamad and Tan, Tian Swee and Mokayed, Hamam and Lai, Khin Wee},
journal={Scientific Reports},
volume={12},
number={1},
pages={19200},
year={2022},
month=11,
day=10,
issn={2045-2322},
url={https://doi.org/10.1038/s41598-022-21848-3},
doi={10.1038/s41598-022-21848-3},
ID={Voon2022}
}

Enviroment

  • Google Colab
  • Google Drive
  • Python3
  • Pytorch

Getting started

Clone the Repo

  • Clone the repo into your Google Colab working directory
!git clone https://github.com/wingatesv/IDC_Grading_Pytorch.git

Datasets Download

FBCG Class Number of Images
Grade 0 588
Grade 1 98
Grade 2 102
Grade 3 82

Train

Run python ./train.py --feature_extractor [BACKBONENAME] [--OPTIONARG]

For example, run python ./train.py --feature_extractor resnet50 --batch_size 16 --temp Temp1 --train_aug --sn reinhard
Commands below follow this example, and please refer to io_utils.py for additional options.

Test

Run python ./test.py --feature_extractor resnet50 --batch_size 16 --temp Temp1 --train_aug --sn reinhard