quip_lung_cancer_detection

This repo contains training and prediction code for 6-class Lung cancer detection using pretrained resnet. The prediction model was trained with LUAD images.

Dependencies

• Pytorch 0.4.0
• Torchvision 0.2.0
• cv2 (3.4.1)
• Openslide 1.1.1
• sklearn
• PIL

Setup conf/variables.sh

• Change the BASE_DIR to the path of your folder after you clone the git repo

Training

• Go to folder "training_codes", run python train_lung_john_6classes.py

WSIs prediction

• Go to folder "scripts", run bash svs_2_heatmap.sh

Docker Instructions

Build the docker image by:

docker build -t luad_detection . (Note the dot at the end).

Step 1:

Create folder named "data" and subfolders below on the host machine:

• data/svs: to contains *.svs files
• data/patches: to contain output from patch extraction
• data/log: to contain log files
• data/heatmap_txt: to contain prediction output
• data/heatmap_jsons: to contain prediction output as json files

Step 2:

• Run the docker container as follows:

nvidia-docker run --name luad-detection -itd -v <path-to-data>:/data -e CUDA_VISIBLE_DEVICES='<cuda device id>' luad_detection svs_2_heatmap.sh 

CUDA_VISIBLE_DEVICES -- set to select the GPU to use

The following example runs the cancer detection pipeline. It will process images in /home/user/data/svs and output the results to /home/user/data.

nvidia-docker run --name luad-detection -itd -v /home/user/data:/data -e CUDA_VISIBLE_DEVICES='0' luad_detection svs_2_heatmap.sh