A volumetric multi-head attention strategy for lung nodule classification in CT

Pulmonary nodules are the principal lung cancer indicator, whose malignancy is mainly related to their size, morphological and textural features. Computational deep representations are today the most common tool to characterize lung nodules but remain limited to capturing nodule variability. In consequence, nodule malignancy classification from CT observations remains an open problem. This work introduces a multi-head attention network that takes advantage of volumetric nodule observations and robustly represents textural and geometrical patterns, learned from a discriminative task. The proposed approach starts by computing 3D convolutions, exploiting textural patterns of volumetric nodules. Such convolutional representation is enriched from a multi-scale projection using receptive field blocks, followed by multiple volumetric attentions that exploit non-local nodule relationships. These attentions are fused to enhance the representation and achieve more robust malignancy discrimination. The proposed approach was validated on the public LIDC-IDRI dataset, achieving a 91.82% in F1-score, 91.19% in sensitivity, and 92.43% in AUC for binary classification. The reported results outperform the state-of-the-art strategy with 3D nodule representations.