Relu Cascade of Feature Pyramid Networks for CT Pulmonary Nodule Detection

Abstract

Screening of pulmonary nodules in computed tomography (CT) is important for early detection and treatment of lung cancer. Many existing works utilize faster RCNN (regions with convolutional neural network or region proposal network) for this task. However, their performance is often limited, especially for detecting small pulmonary nodules (<4 mm). In this work, we propose a new cascade paradigm called “Relu cascade” to detect pulmonary nodules. The training of “Relu cascade” is similar to the conventional cascade learning approach. First, a detection network is trained using limited positive annotations (nodules) and randomly sampled negative samples (background). Then, a second detection network is trained with the same amount of positives and false positives produced by the first network. By repeating this process, multiple detection networks can be trained with subsequent detection networks tuned specifically to eliminate the false positives produced by previous detection networks. The novelty of “Relu cascade” lies in the way of chaining these networks into a cascade. Different from the conventional cascade learning, each level filters out false positive detections independently in the testing phase, which is prone to overfitting as later levels are very specific to a small amount of negative samples. In “Relu cascade”, nodule likelihoods at all previous levels are aggregated, based on which false positives are identified and filtered out. Experimental results on 606 CT scans of different patients show that “Relu cascade” greatly improves the detection performance of conventional cascade learning.