Unsupervised and Weakly-Supervised Learning of Localized Texture Patterns of Lung Diseases on Computed Tomography

Computed tomography (CT) imaging enables in vivo assessment of lung parenchyma and several lung diseases. CT scans are key in particular for the diagnosis of 1) chronic obstructive pulmonary disease (COPD), which is the fourth leading cause of death worldwide, and largely overlaps with pulmonary emphysema; and 2) lung cancer, which is the first leading cause of cancer-related death, and manifests in its early stage with the presence of lung nodules. Most lung CT image analysis methods to-date have relied on supervised learning requiring manually annotated local regions of interest (ROIs), which are slow and labor-intensive to obtain. Machine learning models requiring less or no manual annotations are important for a sustainable development of computer-aided diagnosis (CAD) systems. This thesis focused on exploiting CT scans for lung disease characterization via two learning strategies: 1) fully unsupervised learning on a very large amount of unannotated image patches to discover novel lung texture patterns for pulmonary emphysema; and 2) weakly-supervised learning to generate voxel-level localization of lung nodules from CT whole-slice labels. In the first part of this thesis, we p