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Dual-Attention Network for Acute Pancreatitis Lesion Detection with CT Images

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Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021) (MICAD 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 784))

Abstract

Deep learning technique has been widely applied in medical image analysis, whereas no work has been done for recognition or detection for acute pancreatitis, which is one of the most common digestive disorders. Most of current detection architectures are not sufficiently robust to deal with scale variation of all kinds of acute pancreatitis lesions, resulting in inaccurate detection and sometimes false positive small lesions near large lesions. To address this, we proposed a method that modifies classic detection network by employing the idea of attention mechanism in backbone and detector neck. Specifically, channel-wise attention is used to capture the relationship between channels of feature maps to pale the uninformative and meaningless channels unrelated to AP lesions, and spatial attention is applied to prompting the network focus on the area more relevant to AP lesions. The experiment conducted on a real acute pancreatitis dataset verifies the performance improvement the proposed method brings to the original detection model.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Jinyi Zhang & Daoqiang Zhang

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  1. Jinyi Zhang
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  2. Daoqiang Zhang
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Correspondence to Daoqiang Zhang .

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Editors and Affiliations

  1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China

    Ruidan Su

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Han Liu

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Zhang, J., Zhang, D. (2022). Dual-Attention Network for Acute Pancreatitis Lesion Detection with CT Images. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_25

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  • DOI: https://doi.org/10.1007/978-981-16-3880-0_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3879-4

  • Online ISBN: 978-981-16-3880-0

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