Multi-Scale Multi-View Model Based on Ensemble Attention for Benign-Malignant Lung Nodule Classification on Chest CT

The accurate differential diagnosis of lung nodules is critical in the early screening of lung cancer. Although deep learning-based methods have obtained good results, the large variations in sizes and shapes of nodules restrict further performance improvement in automated diagnosis. In this paper, a multi-scale multi-view model based on ensemble attention (MSMV-EA) is proposed to discriminate the benign and malignant nodules on chest computed tomography (CT). First, the raw CT scans are aligned to a same resolution and a uniform intensity, and multiple sets of input patches with different scales are extracted from nine fixed view angles of each nodule volume. Then, a convolutional neural network (CNN)-based three-branch framework is constructed to fully learn the rich spatial structural information of nodule CT images, and more discriminative representations can be harvested in this way. Finally, an ensemble attention module is developed to adaptively aggregate multi-level deep features produced from different sub-networks, which can boost feature integration efficiency in an end-to-end trainable fashion. Experimental results on the public lung nodule CT image dataset LIDC-IDRI demonstrate that the proposed MSMV-EA method possesses the superior identification performance of benign-malignant nodules compared with some state-of-the-art (SOTA) approaches.