Supervised and Unsupervised Tumor Characterization in the Deep Learning Era

Abstract--Cancer is among the leading causes of death worldwide. Risk stratification of cancer tumors in radiology images can be improved with computer-aided diagnosis (CAD) tools which can be made faster and more accurate. Tumor characterization through CADs can enable noninvasive cancer staging and 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 in deep learning algorithms, particularly by utilizing a 3D Convolutional Neural Network along with 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 scheme to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion (LLP) approaches, we propose a new algorithm, proportion-SVM, to characterize tumor types. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. Cancer affects a significant number of the world's population; approximately 40% of people will be diagnosed with cancer at some point during their lifetime with an overall mortality of 171.2 per 100,000 people per year (based on 2008-2012 deaths) [1]. Lung and pancreatic cancers are two of the most common cancers. While lung cancer is the largest cause of cancer-related deaths in the world, pancreatic cancer has the poorest prognosis with a 5-year survival rate of just 7% in the United States [1]. With regards to pancreatic cancer, specifically in this work, we focus on the challenging problem of automatic diagnosis of Intraductal Papillary Mucinous Neoplasms (IPMN). IPMN is a pre-malignant condition and if left untreated, it can progress to invasive cancer.