Deep Learning model for prostate cancer diagnosis and cancer stage prediction from H&E histology images

Based on internal benchmarking we settled with DenseNet for the cancer stage prediction, and ResNet56 for the cancer/benign classification task.

For details and medical background check our report at BE_223B_final.docx.pdf.

For details, see report.pdf.

Cancer diagnosis (Cancer vs healthy classification)

Running

python benign_cancerous_resnet56_final.py

performs the following tasks

create matrices for the images and for the label of the train, val, test sets
covert to categorical, normalize pixel values, and decide whether to subtract mean
define a function that creates a DenseNet, and a function that prints the learning rate at the end of each epoch
perform grid search for hyperparameters
create a t-SNE plot with PCA preprocessing
perform manual err analysis
save plot of confusion matrices at the tile level and the patient level.

Running

python densenet_3class_cancer.py

implements the following pipeline

create dictionary based on the csv file with the labels
create matrices for train, test data
normalize pixel values in the data and split them into train, validation and test sets
define lr_schedule
define ResNet layer
define ResNetv2 architecture
subtract mean and convert to categorical
create function evaluate_network which has x_train, x_val as global variables, returns only the val_acc, and is solely used for the Bayesian Hyperparameter Optimization
create function create_train_return_model, which fulfills a similar goal as evaluate_network but also retunrs the model, to do manual hyperparameter tuning
implement the Bayesian Hyperparameter Optimization to roughly tune the hyperparameters.
do some further manual tuning of the Hyperparameters using the function create_train_return_model
plot training history for best model, and evaluate on test set
make t-SNE plots
print statements for manual err analysis
plot accuracy vs image size

grad_cam_heat.ipynb shows what features the model uses to make its prediction in one of the false positive diagnoses.