OSANet:A One-Shot Aggregation Network for Pulmonary Nodule Detection
Authors: 
Zhen Wang, Jun LiAuthors Info & Claims
Zhen Wang, Jun LiAuthors Info & ClaimsPages 248 - 251
Abstract
Pulmonary nodules are an early symptom of lung cancer, so pulmonary nodule detection is crucial. Due to the nodules' small size and variable morphology, it is takes work to establish an accurate detection system. Inspired by the practical application of a U-shaped encoder-decoder structure combined with region proposal networks and the advantages of one-shot aggregation (OSA) for small target detection, we proposed a network based on the OSA module for 3D pulmonary nodule detection. In the network, the one-shot aggregation can aggregate intermediate features and filter redundant information. In addition, we have added a receptive field block (RFB) behind the decoder subnet. The RFB block generates multiscale features and expands the receptive field, further improving detection performance. Experimental results on the LUNA16 verify the effectiveness of our proposed model.
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Index Terms
- OSANet:A One-Shot Aggregation Network for Pulmonary Nodule Detection
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Published In
April 2023
296 pages
ISBN:9798400707544
DOI:10.1145/3616901
Copyright © 2023 ACM.
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New York, NY, United States
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Published: 05 March 2024
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FAIML 2023
FAIML 2023: 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning
April 14 - 16, 2023
Beijing, China
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