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Exploiting Saliency in Attention Based Convolutional Neural Network for Classification of Vertical Root Fractures

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Book cover Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12661))

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Abstract

Cone-beam computed tomography (CBCT) is widely used in clinical diagnosis of vertical root fractures (VRFs) which presents as crack on the teeth. However, manually checking the VRFs from a larger number of CBCT images is time-consuming and error-prone. Although the Convolutional Neural Networks (CNN) have achieved unprecedented progress in natural image recognition, end-to-end CNN is unsuitable to identify VRFs due to crack appears to be multi-scales and their complex relationships with surroundings tissues. We proposed a novel Feature Pyramids Attention Convolutional Neural Network (FPA-CNN), which incorporates saliency mask and multi-scale feature to boost the classification performance. Saliency map is viewed as spatial probability map where a person might look first to make a discriminative conclusion. Therefore it plays a role of high-level hint to guide the network focusing on the discriminative region. Experimental results demonstrate that our proposed FPA-CNN overcomes the challenge arised from multi-scale crack and complex contextual relationships.

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Authors and Affiliations

  1. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhenxing Xu, Peng Wan & Daoqiang Zhang

  2. Affiliated Stomatology Hospital of Medical School, Nanjing University, Nanjing, China

    Gulibire Aihemaiti

Authors
  1. Zhenxing Xu
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  2. Peng Wan
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  3. Gulibire Aihemaiti
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  4. Daoqiang Zhang
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Corresponding author

Correspondence to Daoqiang Zhang .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Xu, Z., Wan, P., Aihemaiti, G., Zhang, D. (2021). Exploiting Saliency in Attention Based Convolutional Neural Network for Classification of Vertical Root Fractures. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_29

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  • DOI: https://doi.org/10.1007/978-3-030-68763-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68762-5

  • Online ISBN: 978-3-030-68763-2

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