Pipeline for training and validating convolutional neural network (CNN) models to infer molecular features from pathological whole slide images (WSIs).
The entire workflow comprises three steps:
- Tessellating the WSI into image tiles;
- Classifying the image tiles into five tissue types using a developed tissue type classifier;
- Tiles sampling and CNN model training, validation and test.
The first step is implemented in MATLAB R2018b with the OpenSlide library.
The other two steps are implemented in python 3.8 with the following packages:
joblib==1.0.0
numpy==1.19.1
opencv-python==4.3.0.36
openslide-python==1.1.2
pytorch==1.9.1
sklearn==0.0
tensorboard==2.4.1
tensorboardx==2.1
torchvision==0.10.1