Master thesis project

Thesis title:
Deep Learning Application in Ovarian Tumor Malignancy Classification

In this repository I did few things:

• preprocessed dataset (file clean_dataset.py)
• code to calculate few useful metrics (metrics.py)
• code for CV - nested k-fold approach (nested_kfold.py)
• tested old known techniques on our dataset (file cv_old_models.py)
• prepared experiment using ladder network - semi-supervised approach (file cv_ladder.py)
• prepared experiment using new deep learning techniques - supervised approach (file cv_dl.py)
• file to predict new patients using best created model (file predict.py)

Installation

All libraries with needed version are in requirements.txt file. To run experiments faster please set up flags:

export THEANO_FLAGS=mode=FAST_RUN,floatX=float32

CV results

All results are in ./results directory (split into ladder - semi-supervised approach and dl - supervised one). To get best model fold by fold results just type

./cv_ladder.py --get-cv-results

or

./cv_dl.py --get-cv-results

Retrain all models

• ladder

./cv_ladder.py --train
./cv_ladder.py --train-best

• dl

./cv_dl.py --train
./cv_dl.py --train-best

Where train - finds best model for each nested fold and gets its results and train-best - finds best model for whole dataset using same grid and creates best model

Predicting

There is a script to predicting new patients. To use it just type:

./predict.py Color Ca125 AgeAfterMenopause

Where features are:

• Color - IOTA Amount of blood flow 1 / 2 / 3 / 4
• Ca125 - The blood serum marker
• AgeAfterMenopause - how many years after menopause (0 if menopause didn't occurred)