A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images. This is the code used in the paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks"
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DeepSlide: A Sliding Window Framework for Classification of High Resolution Microscopy Images (Whole-Slide Images)

By Jason Wei, Behnaz Abdollahi, and Saeed Hassanpour

This repository is a sliding window framework for classification of high resolution whole-slide images, often called microscopy or histopathology images. This is also the code for the paper Pathologist-level Classification of Histologic Patterns on Resected Lung Adenocarcinoma Slides with Deep Neural Networks. For a practical guide and implementation tips, see the medium post Classification of Histopathology Images with Deep Learning: A Practical Guide.

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Take a look at code/config.py before you begin to get a feel for what parameters can be changed.

1. Train-Val-Test Split:

Splits the data into a validation and test set. Default validation whole-slide images (WSI) per class is 15 and test images per class is 30. You can change these numbers by changing val_wsi_per_class and test_wsi_per_class in code/config.py. You can skip this step if you did a custom split (for example, you need to split by patients).

python code/1_split.py

If you do not want to duplicate the data, set keep_orig_copy in code/config.py to False.

Inputs: all_wsi

Outputs: wsi_train, wsi_val, wsi_test, labels_train.csv, labels_val.csv, labels_test.csv

2. Data Processing

  • Generate patches for the training set.
  • Balance the class distribution for the training set.
  • Generate patches for the validation set.
  • Generate patches by folder for WSI in the validation set.
  • Generate patches by folder for WSI in the testing set.
python code/2_process_patches.py

Note that this will take up a significant amount of space. Edit num_train_per_class in config.py to be smaller if you wish to not generate as many windows. If your histopathology images are H&E-stained, whitespace will automatically be filtered. Turn this off in type_histopath in code/config.py. Default overlapping area is 1/3 for test slides. Use 1 or 2 if your images are very large; you can also change this in slide_overlap in code/config.py.

Inputs: wsi_train, wsi_val, wsi_test

Outputs: train_folder (fed into model for training), patches_eval_train (for validation, sorted by WSI), patches_eval_test (for testing, sorted by WSI)

3. Model Training

CUDA_VISIBLE_DEVICES=0 python code/3_train.py

We recommend using ResNet-18 if you are training on a relatively small histopathology dataset. You can change hyperparameters in code/config.py. There is an option to retrain from a previous checkpoint. Model checkpoints are saved by default every epoch in checkpoints.

Inputs: train_folder

Outputs: checkpoints, logs

4. Testing on WSI

Run the model on all the patches for each WSI in the validation and test set.

CUDA_VISIBLE_DEVICES=0 python code/4_test.py

We automatically choose the model with the best validation accuracy. You can also specify your own. You can change the thresholds used in the grid search by editing threshold_search in code/config.py.

Inputs: patches_eval_val, patches_eval_test

Outputs: preds_val, preds_test

5. Searching for Best Thresholds

The simplest way to make a whole-slide inference is to choose the class with the most patch predictions. We can also implement thresholding on the patch level to throw out noise. To find the best thresholds, we perform a grid search. This function will generate csv files for each WSI with the predictions for each patch.

python code/5_grid_search.py

Inputs: preds_val, labels_val.csv

Outputs: inference_val

6. Visualization

A good way to see what the network is looking at is to visualize the predictions for each class.

python code/6_visualize.py

Inputs: wsi_val, preds_val

Outputs: vis_val

You can change the colors in colors in code/config.py

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7. Final Testing

Do the final testing to get the confusion matrix on the test set.

python code/7_final_test.py

Inputs: preds_test, labels_test.csv, inference_val and labels_val (for the best thresholds)

Outputs: inference_test and confusion matrix to stdout

Best of luck.

Quick Run

If you have utter trust in this code and do not want to see the outputs at each step, do:

sh code/run_all.sh

Pre-Processing Scripts

See code/z_preprocessing for some code to convert images from svs into jpg. This uses OpenSlide and takes a while. How much you want to compress images will depend on the resolution that they were originally scanned, but a guideline that has worked for us is 3-5 MB per WSI.

Still not working? Consider the following...

  • Ask a pathologist to look at your visualizations.
  • Make your own heuristic for aggregating patch predictions to get WSI classification. Often, a slide thats 20% abnormal and 80% normal should be classified as abnormal.
  • If each WSI can have multiple types of lesions/labels, you may need to annotate bounding boxes around these.
  • Did you pre-process your images? If you used raw .svs files that are more than 1GB in size, its likely that the patches are way too zoomed in to see any cell structures.
  • If you have less than 10 WSI per class in the training set, get more.
  • Normalizing color channels with custom values. You can change this in utils_model.py.
  • Feel free to view our end-to-end attention-based model: https://arxiv.org/abs/1811.08513.

Future Work

  • Contributions to this repository are welcome.
  • Code for generating patches on the fly instead of storing them in memory for training and testing would save a lot of disk space.
  • If you have issues, please post in the issues section and we will do our best to help.


DeepSlide is an open-source library and is licensed under the GNU General Public License (v3). For questions contact Saeed Hassanpour at Saeed.Hassanpour@dartmouth.edu. If you are using this library please cite:

Jason Wei, Laura Tafe, Yevgeniy Linnik, Louis Vaickus, Naofumi Tomita, Saeed Hassanpour, "Pathologist-level Classification of Histologic Patterns on Resected Lung Adenocarcinoma Slides with Deep Neural Networks", Scientific Reports, In Press.