Weak Segmentations and Ensemble Learning to Predict Semantic Ratings of Lung Nodules

Computer-aided diagnosis (CAD) can be used as "second readers" in the imaging diagnostic process. Typically to create a CAD system, the region of interest (ROI) has to be first detected and then delineated. This can be done either manually or automatically. Given that manually delineating ROIs is a time consuming and costly process, we propose a CAD system based on multiple computer-derived weak segmentations (WSCAD) and show that its diagnosis performance is at least as good as the predictions developed using manual radiologist segmentations. The proposed CAD system extracts a set of image features from the weak segmentations and uses them in an ensemble of classification algorithms to predict semantic ratings such as malignancy. These automated results are compared against a reference truth based on ratings and segmentations provided by radiologists to determine if it is necessary to obtain manual radiologist segmentations in order to develop a CAD. By developing a pair of CADs using the Lung Image Database Consortium (LIDC) data, we show that WSCADs are at least as accurate in predicting semantic ratings as CADs based on radiologist segmentation.