False positive reduction in lymph node detection by using convolutional neural network with multi-view input

The presence of enlarged lymph nodes is a signal of malignant disease or infection. Lymph nodes detection plays an important role in clinical diagnostic tasks. Previous lymph nodes detection methods achieve high sensitivity at the cost of a high false positive rate. In this paper, we propose a method that helps reject false positives. Features are extracted separately from 2D CT slices by using a deep convolutional neural network with multi-view input. Separated feature layers can extract the most suitable features from each input slice individually. We validate the approach on a public dataset and improve the sensitivity by reducing the false positive rate.