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Pathologist-level interpretable whole-slide cancer diagnosis with deep learning, nature machine intelligence

The overall pipeline has multiple steps and involves large-size whole slide image processing. Using the code requires users to have basic knowledge about python programming, Tensorflow, and training deep neural networks in order to understand the whole training and evaluation procedures.

1. Data preparation

Generate II-Image data from whole slides

  • See the dataset info in the paper to get download link of the dataset. The user can also use the script download_nmi_wsi_data.sh under download directory to download the dataset.

  • Download whole slide data to data/Slide/. Download report data to data/report.

  • anno_parser/ provides tools to read patches from whole slide images based on annotations for the following segmentation and classification task. Refer the README in anno_parser to obtain more details. Users need to sample 1024x1024 patches and then resize them to 256x256 (as described in the paper). The number of generated images are shown in Fig.2e of the paper (we use the Keras ImageGenerator, so we need to follow the loader requirement to organize the data. See the loader in the corresponding folders to understand the details). Users can sample around the same number of images and organize the data into two types of hierarchies for segmentation and classification.

  • Save training images to data/segmentation and organize data like the following for segmentation. The image and groundTruth contain subdirectories {1/2/3}, which store each category's images and annotation masks, respectively. Class 1 is low grade, class 2 is high grade, and class 3 is merged normal and insufficient information (see paper and anno_parser/ folder for more details).

    • train/
      • image/
        • 1/
        • 2/
        • 3/
      • groundTruth/
        • 1/
        • 2/
        • 3/
    • test/
      • image/
        • 1/
        • 2/
        • 3/
      • groundTruth/
        • 1/
        • 2/
        • 3/

  • Building a data folder alias data/classification pointing to data/segmentation

    ln -s data/segmentation data/classification

  • Organize whole slide data to data/wsi, split the slides files under data/Slide/Img into data/wsi/{train/test}_slides folders based on json files under data/Slide/.

2. Train s-net

  • Go to segmentation folder 
    cd segmentation
  • Prepare your data to fit segmentation.data_gen.data_loader. As shown in the paper, we ignore the pixels without annotation. Read the code and README.md in anno_parser/ for more details. Note that, we use a mask value 44 for ignored pixels, and 255 and 155 for positive and negative values, respectively.
  • Train the model 
    device=0 sh train.sh
  • Evaluate the model 
    device=0 sh test.sh

3. Segment whole slides and generate ROI

ROIs are generated for the usage of training and evaluation the a-net. Users need to select model and point to --load_from_checkpoint in wsi_deploy.sh

cd segmentation
start=0 end=${tot-train-slides} device=0 split=train sh wsi_deploy.sh
start=0 end=${tot-test-slides} device=0 split=test sh wsi_deploy.sh

tot-train-slides is the total number of slides. Read seg_wsi.py for more details and how to sample ROI. Results will be saved in $res_dir defined in seg_wsi.py as well as wsi_deploy.sh

4. Train d-net

Pre-train the image model on data in data/classification

  • Train the model 
    cd classification
device=0 sh train.sh
  • Optionally, test the model (CHECK all the checkpoint path first in train.sh) 
    device=0 sh test.sh
  • Note that put the trained checkpoint.h5 (users may need to do early stopping for model selection to prevent overfitting) into classification/trained_model and modify topic_mdnet_train.py line 75 to refer the pretrained CNNs.

Train the full model

  • Train the model 
    device=0 sh scripts/topic_mdnet_train.sh
  • Test the model (CHECK all the checkpoint path first in scripts/topic_mdnet_train.sh) for generate reports 
    device=0 sh scripts/topic_mdnet_eval.sh

Generate IV-Diagnosis dataset

Users need to extract features of ROIs generated in Step 3. Please modify the path details in the extract_feat.py to point to folder where ROI are saved, i.e. checkpoints/seg_{train/test}_slides/.

device=0 sh scripts/extract_feat.sh

Generatded .h5 files save features for last step is also in the same folder

5. Train a-net

  • Train 
     device=0 sh scripts/mlp_train.sh
  • Test the model 
     device=0 sh scripts/mlp_eval.sh

Citation

Please cite our paper if you use the data or code

@article{zhang2019pathologist,
  title={Pathologist-level interpretable whole-slide cancer diagnosis with deep learning},
  author={Zhang, Zizhao and Chen, Pingjun and McGough, Mason and Xing, Fuyong and Wang, Chunbao and Bui, Marilyn and Xie, Yuanpu and Sapkota, Manish and Cui, Lei and Dhillon, Jasreman and others},
  journal={Nature Machine Intelligence},
  volume={1},
  number={5},
  pages={236},
  year={2019},
  publisher={Nature Publishing Group}
}