AI grading

This repository is for the paper "The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma" published in Nature Cancer. It could guide you to generate growth pattern masks with a well-trained deep learning model for semantic segmentation, from which the proportion of each growth pattern can be obtained, thereby replicating IASLC grading for lung adenocarcinoma.

Generating tiles for a whole slie image

Dependencies for generating_tiles are in AIgraind/generating_tiles/requirements, following the step in the work of AbdulJabbar, K. et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nature Medicine (2020). doi: 10.1038/s41591-020-0900-x

After all dependencies are well installed, if the format of whole slide images are in .svs format, then

python ./generating_tiles/main_tiles.py -d /path/to/raw/slides -o /path/to/result -p '*.svs'

The ouptut structure will be

result_cws_tiling/
        ├── TCGA-xxxx-xxxx.svs
		├── Da0.jpg
		├── Da1.jpg
		└── ...
	├── TCGA-xxxx-xxxx.svs
		├── Da0.jpg
		├── Da1.jpg
		└── ...

Training

Dependencies for training_patch are in AIgrading/requirements.

Dataset for training: https://doi.org/10.5281/zenodo.10016027

Step0: creat the conda environment following AIgrading/requirements.txt

Step1: divide image in trainset (download from 10.5281/zenodo.10016027) into patches with a size of $768 \times 768$

python ./training_patch/img2patch.py --image_path /path/to/training/image --label_path /path/to/training/mask --save_path /path/to/patches

The output training patches from this step are structured as

Training_patches /
├── image
│   ├── train001_xxx_0.png
│   ├── train001_xxx_1.png
│   └── ...
└── maskPng
    ├── train001_xxx_0.png
    ├── train001_xxx_1.png
    └── ...

Step2: train the model

python ./training_patch/train_main.py --input_dir /path/to/patches/image --target_dir /path/to/patches/maskPng --img_size 384 --num_class 7 --batch_size 8 --num_epoch 60

Inference

Dependencies for inference_slide are in AIgraind/requirements, same with the training

Input: H&E image tiles

Output: Growth pattern mask,

#000000 black-background, 
#0000ff blue-lepidic, 
#ffff00 yellow-papillary, 
#ff0000 red-acinar, 
#00ffff cyan-cribriform, 
#ff00ff magenta-micropapillary, 
#880000 dark red-solid

python ./inference_slide/main_gp.py -d /path/to/cws_tiling -o /path/to/cws_mask -s /path/to/ss1_mask -sf /path/to/ss1_mask_final -p *.svs -n 0