AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection (Student Abstract)

Authors

  • Haoyang Chen National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, China
  • Shuai Liu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, China
  • Feng Lu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, China
  • Wei Li Centre for Distributed and High Performance Computing School of Computer Science, The University of Sydney, Australia
  • Bin Sheng Department of Computer Science and Engineering, Shanghai Jiao Tong University
  • Mi Li Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
  • Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, China
  • Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science, The University of Sydney, Australia

DOI:

https://doi.org/10.1609/aaai.v37i13.26953

Keywords:

Transformer, Early ONFH Detection, FGVC

Abstract

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly.
AAAI-23 Proceedings Cover

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Published

2023-09-06

How to Cite

Chen, H., Liu, S., Lu, F., Li, W., Sheng, B., Li, M., Jin, H., & Zomaya, A. Y. (2023). AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16186-16187. https://doi.org/10.1609/aaai.v37i13.26953

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program