Deep Active Learning for Effective Pulmonary Nodule Detection

Abstract

Expensive and time-consuming medical imaging annotation is one of the big challenges for the deep learning-based computer-aided diagnosis (CAD) on the low-dose computed tomography (CT). To address this problem, we propose a novel active learning approach to improve the training efficiency for a deep network-based lung nodule detection framework as well as reduce the annotation cost. The informative CT scans, such as the samples that inconspicuous or likely to produce high false positives, are selected and further annotated for the nodule detector network training. A simple yet effective schema suggests the samples by ranking the uncertainty loss predicted by multi-layer feature maps and the Region of Interests (RoIs). The proposed framework is evaluated on a public dataset DeepLesion and achieves results that surpass the active learning baseline schema at all the training cycles.