Workflow
-
Tile WSI's into image tiles File: wsi_into_tiles/main.py
- Get a list of WSI's where there tumors are detected (this can be obtained from the Camelyon17 database. We name this file "positive_slides.txt"
- Feed "positive_files.txt" into main.py in the wsi_into_tiles directory -- Filter each WSI and select based on cell density -- For each WSI, save the tile_summary object into a list -- Feed each tile_summary into map_tumor module, which will map out the tumor region on each tile (if it exists). Save all tiles as images. -- Write list of tumor-positive tiles and tumor region coordinates into a dump file called "annotations.txt"
-
Check tile images File: resnet/check_size.py
- Check each tile image to ensure they are of proper size
- Consider deleting or checking identified tiles
-
Prepare for training File: resnet/split.py; resnet/negative_tiles.py; images_mean_std.py
- Split the tile images into different cross-validation and tumor sets
- Save negative tiles into each train and val nontumor directories
- Calculate images mean
-
Train resnet50 File: train.py
- Train a resnet50 model