Active Learning for Pulmonary Nodule Detection

Expensive and time-consuming medical imaging annotation
is one of the big challenges for the deep-learning-based computer-aided
diagnose (CAD) nodule detection system on the low-dose computed
tomography (CT). To address this, we propose a novel active learning
approach to improve the training efficiency for a deep-network-based lung
nodule detection framework as well as reducing the annotation cost. The
informative CT scans, such as the samples that inconspicuous or likely to
produce higher false positives rates, are selected and further annotated
by the radiologists for the nodule detector network training. A simple yet
effective schema suggests the sample by ranking the uncertainty loss
predicted from global scan-level and local proposal-level perspective through
multi-layer feature maps from the backbone network and the region of
interests (ROIs) from the detector network respectively.

• Machine Perception