Paper: An end-to-end and well-generalized weakly supervised whole slide image analysis for lung cancer classification using deep learning

• Framework
• Ecosystem
• QuickStart
  • Install
  • Usage
• Contribution
• Maintainers

• Weakly supervised learning (No expensive annotations)
• End-to-end pipeline (Iterative sampling and feature aggregation module)
• Data efficiency (<200 images, AUCs of >0.9)
• Promising generalizability (six heterogenous external datasets with AUCs of 0.93-0.97)
• Overperform state-of-the-art methods

System: Linux(CentOS) Pytorch (version 1.7.0): model training and validation. OpenSlide (version 3.4.1): processing whole slide images. lmdb (version 1.3.0): building lmdb dataset for faster data loading. staintools: following https://github.com/Peter554/StainTools.

$ git clone https://github.com/DeepLaboratory/AlphaPatho.git # install AlphaPatho
$ cd AlphaPatho

Directory description:

├─ data.py                 // dataset code
├─ mean_pixel.pkl		   // mean value of pixel value for data augmentation
├─ model.py				   // model architecture
├─ run.py		           // main code for training and validation
├─ run.sh                  // run.py launcher
├─ test.py                 // main code for inference or evaluation
├─ run_test.sh             // test.py launcher
├─ utils.py                // tool code
├─ tmp.py                  // LMDB dataset building code

• Slide tiling: see methods in our paper. When tiling is done, we should have files like below:

  ├─ slide1_folder
  │  ├─ tile1.png
  │  ├─ tile2.png
  │  ├─ ...
  ├─ slide2_folder
  │  ├─ tile1.png
  │  ├─ tile2.png
  │  ├─ ...
  ├─ ...
  

• csv for LMDB dataset generation: after slide tiling, we need to generate a csv used for LMDB dataset generation. The csv content is as follow:

  slides_name	label
  xxx/slide1_folder	1
  xxx/slide2_folder	0
  ... ...	...

• Building LMDB dataset

  tmp.py is the main code. We need to modify the csv path and lmdb dataset save directory. Using tmp.py, we can generate the LMDB dataset.

• Dataset preparation: preparing csv files for 5-fold validation, the format of each csv file is like follows:

  slides_name	label
  xxx/slide1_folder	1
  xxx/slide2_folder	0
  ... ...	...

  After cvs preparation, we will get train.csv, val.csv, and test.csv for each fold.

• Model training: run run.sh and start training.

Preparing datasets for testing, modifying the parameters in the run_test.sh, and runing run_test.sh for testing!

• [raycaohmu]https://github.com/raycaohmu)