  Risk stratification (characterization) of tumors from radiology images can be
more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor
characterization through such tools can also enable non-invasive cancer
staging, prognosis, and foster personalized treatment planning as a part of
precision medicine. In this study, we propose both supervised and unsupervised
machine learning strategies to improve tumor characterization. Our first
approach is based on supervised learning for which we demonstrate significant
gains with deep learning algorithms, particularly by utilizing a 3D
Convolutional Neural Network and Transfer Learning. Motivated by the
radiologists' interpretations of the scans, we then show how to incorporate
task dependent feature representations into a CAD system via a
graph-regularized sparse Multi-Task Learning (MTL) framework. In the second
approach, we explore an unsupervised learning algorithm to address the limited
availability of labeled training data, a common problem in medical imaging
applications. Inspired by learning from label proportion (LLP) approaches in
computer vision, we propose to use proportion-SVM for characterizing tumors. We
also seek the answer to the fundamental question about the goodness of "deep
features" for unsupervised tumor classification. We evaluate our proposed
supervised and unsupervised learning algorithms on two different tumor
diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans,
respectively, and obtain the state-of-the-art sensitivity and specificity
results in both problems.
