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• W2980970703 abstract "Since the diagnosis of lung cancer requires a careful investigation of each suspicious nodule, traditional detection approaches need to integrate the information of all nodules. In deep learning, the convolutional block attention module can be integrated into convolutional neural network architectures for adaptive feature refinement. Therefore, we propose a state-of-the-art pulmonary detection network, which embeds this attention module. Furthermore, the bounding box regression is redefined to enhance convergence ability. The novel detection network is validated on a public dataset from LUNA16, which achieves 0.854 sensitivity rate." @default.
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• W2980970703 date "2019-07-01" @default.
• W2980970703 modified "2023-09-30" @default.
• W2980970703 title "Pulmonary Nodule Detection Based on Convolutional Block Attention Module" @default.
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• W2980970703 doi "https://doi.org/10.23919/chicc.2019.8865792" @default.
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