Learning Interstitial Lung Diseases CT Patterns from Reports Keywords

The interpretation of CT exams from patients with interstitial lung diseases depends on the correct assessment of associated CT patterns. Computer aided diagnosis systems often study the automatic identification of CT patterns, using the division of the lung in volumes of interest and the use of supervised classification. Despite moderate success, this approach has been hampered by the shortage of medical annotations available to research groups. We propose a new method that collects exams that contain CT patterns through the presence of keywords in radiology reports, to learn pattern models using a multiple instance learning algorithm. We compared our approach to the traditional use of volumes of interest annotations for six interstitial lung diseases patterns. The results show our approach performed comparatively in four of the studied patterns, and poorly for the other two. The results suggest that under certain conditions learning CT patterns from radiology reports is possible, which could foster developments in computer aided diagnosis systems.