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Recommend the top k lesion cells and predict the positive probability of WSIs.
GPU: Nvidia 1080Ti or better (at least 10G memory)
CPU: Intel i7 or better
System Memory: 16G or better
System: Win10
Nvidia GPU corresponding driver
CUDA: cuda 9.0
cudnn: cudnn 7.0
Python: 3.6
Tensorflow-gpu: 1.7.0
Tensorboard: 1.7.0
Keras: 2.1.2
Keras-Applications: 1.0.6
Numpy: 1.19.5
Openslide-python: 1.1.1
Opencv-python: 3.4.1.15
Pandas: 0.20.3
Scikit-image: 0.17.2
Scikit-learn: 0.23.2
WSI formats supported by the opensource OpenSlide library, including x.svs
, x.mrxs
, x.tif
, etc;
WSI resolution: 20× or 40× (0.1 – 0.6 um/pixel, 0.1 – 0.4 um/pixel is better)
Installation
Step1. Install CUDA v9.0 and cuDNN v7.0.5
Step2. Download Aided-Diagnosis-System-for-Cervical-Cancer-Screening
git clone git@github.com:ShenghuaCheng/Aided-Diagnosis-System-for-Cervical-Cancer-Screening.git
cd Aided-Diagnosis-System-for-Cervical-Cancer-Screening
Step3. Install requirements
pip3 install -U pip && pip3 install -r requirements.txt
Train
# train model1 classifier
python tools/train.py -n model1-cls -b 16
# train model1 locator based on model1 classifier's backbone
python tools/train.py -n model1-loc -b 16 -w [path to model1-cls weight]
# train model2 classifier
python tools/train.py -n model2-cls -b 32
# train WSI classifier
python tools/train.py -n wsi-cls -b 64
Evaluation
Evaluate classifiers.
python tools/eval.py -n model1-cls -b 16 -w [path to evaluated weight]
model2-cls -b 32
wsi-cls -b 64
Inference
Do inference to WSIs according to config file.
python tools/inference.py -c configs/wsi_inference.py -f [path to WSI or path to WSI list files] [--intermediate]
Prepare: convert h5
weights to pb
files.
python tools/convert_to_pb.py -m model1 -w [path to weights] -o [path to save]
model2
wsi_clf_top10
wsi_cls_top20
wsi_clf_top30
Do inference: see C++ software
Tutorials
- For dataset config file, see config for dataset
- For train and eval config file, see configs
- For inference config file, see configs
The C++ software with test WSIs is available at Baidu Cloud.
Correspongding user manual pdf is uploaded.
Codes can be provided by email xlliu@mail.hust.edu.cn or chengshen@hust.edu.cn.
If our work is useful for your research, please consider citing our paper:
Cheng, S., Liu, S., Yu, J. et al. Robust whole slide image analysis for cervical cancer screening using deep learning. Nat Commun 12, 5639 (2021). https://doi.org/10.1038/s41467-021-25296-x
@article{cheng2021robust,
title={Robust whole slide image analysis for cervical cancer screening using deep learning},
author={Cheng, Shenghua and Liu, Sibo and Yu, Jingya and Rao, Gong and Xiao, Yuwei and Han, Wei and Zhu, Wenjie and Lv, Xiaohua and Li, Ning and Cai, Jing and others},
journal={Nature communications},
volume={12},
number={1},
pages={1--10},
year={2021},
publisher={Nature Publishing Group}
}