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

• URL: http://arxiv.org/abs/2303.03625v1
• Date: Tue, 7 Mar 2023 03:17:49 GMT
• Title: SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
• Authors: Rui Xu, Zhi Liu, Yong Luo, Han Hu, Li Shen, Bo Du, Kaiming Kuang, Jiancheng Yang
• Abstract summary: Lung cancer is the leading cause of cancer death worldwide. Current pulmonary nodule detection methods are usually domain-specific. We propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks.
• Score: 47.44114201293201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective. However, the current pulmonary nodule detection methods are usually domain-specific, and cannot satisfy the requirement of working in diverse real-world scenarios. To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks. This attention module works in the axial, coronal, and sagittal directions. In each direction, we divide the input feature into groups, and for each group, we utilize a universal adapter bank to capture the feature subspaces of the domains spanned by all pulmonary nodule datasets. Then the bank outputs are combined from the perspective of domain to modulate the input group. Extensive experiments demonstrate that SGDA enables substantially better multi-domain pulmonary nodule detection performance compared with the state-of-the-art multi-domain learning methods.

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