AttentNet: Fully Convolutional 3D Attention for Lung Nodule Detection

Abstract Motivated by the increasing popularity and success of attention mechanisms, we observe that popular convolutional attention models like Squeeze-and-Excite (SE) and Convolutional Block Attention Module (CBAM) rely on expensive multi-layer perceptron (MLP) layers. These MLP layers significantly increase computational complexity, making such models less applicable to 3D image contexts, where the data dimensionality and computational costs are inherently higher. In 3D medical imaging, such as pulmonary CT scans, efficient processing is crucial due to the large volume of data. Traditional 2D attention mechanisms, when generalized to 3D, increase the computational load, creating a demand for more efficient attention mechanisms that can operate effectively within a 3D tasks. In this work, we investigate the possibility of incorporating fully convolutional attention within 3D context. Particularly, we present two 3D fully convolutional attention blocks in which we demonstrate effectiveness within the 3D context. We demonstrate effectiveness of the proposed method using the pulmonary CT scans for 3D lung nodule detection. Building on existing 3D nodule detection methods, we present AttentNet, an automated lung nodule detection framework from CT images that performs detection as an ensemble of two stages, candidate proposal stage (Region Proposal Network), in which a high number of candidates is produced, and a false positive reduction stage to reduce the number of false alarms. Throughout our work, we compare the proposed 3D convolutional attention blocks to a number of popular 2D convolutional attention mechanisms by generalizing them to 3D modules, including SE units and CBAM channel attention and spatial attention units. We also compare these to Transformer self-attention units. For the False positive reduction stage, we incorporate a joint analysis approach that considers the variable nodule morphology by aggregating spatial information from different contextual levels. We use LUNA-16 lung nodule detection dataset to demonstrate the benefits of using the proposed fully convolutional attention blocks comparing to baseline popular lung nodule detection methods where no attention is used. It is worth noting that our work does not aim at achieving state-of-the-art results in the lung nodule detection task, rather to demonstrate the benefits of incorporating fully convolutional attention within a 3D context.