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DeepLOC: Deep Learning-Based Bone Pathology Localization and Classification in Wrist X-Ray Images

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Abstract

In recent years, computer-aided diagnosis systems have shown great potential in assisting radiologists with accurate and efficient medical image analysis. This paper presents a novel approach for bone pathology localization and classification in wrist X-ray images using a combination of YOLO (You Only Look Once) and the Shifted Window Transformer (Swin) with a newly proposed block. The proposed methodology addresses two critical challenges in wrist X-ray analysis: accurate localization of bone pathologies and precise classification of abnormalities. The YOLO framework is employed to detect and localize bone pathologies, leveraging its real-time object detection capabilities. Additionally, the Swin, a transformer-based module, is utilized to extract contextual information from the localized regions of interest (ROIs) for accurate classification.

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Acknowledgements

The work was supported by Ministry of Science and Higher Education grant No. 075-10-2021-068.

Author information

Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow, Russia

    Razan Dibo, Andrey Galichin, Dmitry V. Dylov & Oleg Y. Rogov

  2. Artificial Intelligence Research Institute (AIRI), Moscow, Russia

    Oleg Y. Rogov

  3. Pirogov National Medical and Surgical Center, Moscow, Russia

    Pavel Astashev

Authors
  1. Razan Dibo

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  2. Andrey Galichin

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  3. Pavel Astashev

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  4. Dmitry V. Dylov

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  5. Oleg Y. Rogov

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Corresponding author

Correspondence toOleg Y. Rogov.

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  3. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  4. American University of Armenia, Yerevan, Armenia

    Habet Madoyan

  5. Artificial Intelligence Research Institute, Moscow, Russia

    Ilya Makarov

  6. University of Hamburg, Hamburg, Germany

    Irina Nikishina

  7. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  8. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Maxim Panov

  9. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  10. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  11. Apptek, Aachen, Germany

    Evgenii Tsymbalov

  12. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

  13. MTS AI, Moscow, Russia

    Sergey Zagoruyko

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Dibo, R., Galichin, A., Astashev, P., Dylov, D.V., Rogov, O.Y. (2024). DeepLOC: Deep Learning-Based Bone Pathology Localization and Classification in Wrist X-Ray Images. In: Ignatov, D.I.,et al. Analysis of Images, Social Networks and Texts. AIST 2023. Lecture Notes in Computer Science, vol 14486. Springer, Cham. https://doi.org/10.1007/978-3-031-54534-4_14

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