Machine Learning Models on Prognostic Outcome Prediction for Cancer Images with Multiple Modalities
Abstract
Machine learning algorithms have been applied to predict different prognostic outcomes
for many different diseases by directly using medical images. However, the higher
resolution in various types of medical imaging modalities and new imaging feature
extraction framework brings new challenges for predicting prognostic outcomes.
Compared to traditional radiology practice, which is only based on visual interpretation
and simple quantitative measurements, medical imaging features can dig deeper within
medical images and potentially provide further objective support for clinical decisions. In
this dissertation, we cover three projects with applying or designing machine learning
models on predicting prognostic outcomes using various types of medical images.