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Lung cancer is the leading cause of cancer death worldwide. The best solutionfor lung cancer is to diagnose the pulmonary nodules in the early stage, whichis usually accomplished with the aid of thoracic computed tomography (CT). Asdeep learning thrives, convolutional neural networks (CNNs) have beenintroduced into pulmonary nodule detection to help doctors in thislabor-intensive task and demonstrated to be very effective. However, thecurrent pulmonary nodule detection methods are usually domain-specific, andcannot satisfy the requirement of working in diverse real-world scenarios. Toaddress this issue, we propose a slice grouped domain attention (SGDA) moduleto enhance the generalization capability of the pulmonary nodule detectionnetworks. This attention module works in the axial, coronal, and sagittaldirections. In each direction, we divide the input feature into groups, and foreach group, we utilize a universal adapter bank to capture the featuresubspaces of the domains spanned by all pulmonary nodule datasets. Then thebank outputs are combined from the perspective of domain to modulate the inputgroup. Extensive experiments demonstrate that SGDA enables substantially bettermulti-domain pulmonary nodule detection performance compared with thestate-of-the-art multi-domain learning methods.

SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention