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Ensemble Model of Visual Transformer and CNN Helps BA Diagnosis for Doctors in Underdeveloped Areas

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Computer Vision – ACCV 2022 Workshops (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13848))

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Abstract

The diagnosis of Biliary Atresia (BA) is still complicated and high resource consumed. Though sonographic gallbladder images can be used as an initial detection tool, lack of experienced experts limits BA infants to be treated timely, resulting in liver transplantation or even death. We developed a diagnosis tool by ViT-CNN ensemble model to help doctors in underdeveloped area to diagnose BA. It performs better than human expert (with 88.1% accuracy versus 87.4%, 0.921 AUC versus 0.837), and still has an acceptable performance on severely noised images photographed by smartphone, providing doctors in clinical facilities with outdated Ultrasound instruments a simple and feasible solution to diagnose BA with our online tool

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, China

    Zhenghao Wei

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  1. Zhenghao Wei
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Correspondence to Zhenghao Wei .

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Editors and Affiliations

  1. University of Tokyo, Tokyo, Japan

    Yinqiang Zheng

  2. Hacettepe University, Ankara, Türkiye

    Hacer Yalim Keleş

  3. Data61/CSIRO, Canberra, ACT, Australia

    Piotr Koniusz

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Wei, Z. (2023). Ensemble Model of Visual Transformer and CNN Helps BA Diagnosis for Doctors in Underdeveloped Areas. In: Zheng, Y., Keleş, H.Y., Koniusz, P. (eds) Computer Vision – ACCV 2022 Workshops. ACCV 2022. Lecture Notes in Computer Science, vol 13848. Springer, Cham. https://doi.org/10.1007/978-3-031-27066-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-27066-6_6

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