MTS-Net: Dual-Enhanced Positional Multi-Head Self-Attention for 3D CT Diagnosis of May-Thurner Syndrome
<|reference_start|>MTS-Net: Dual-Enhanced Positional Multi-Head Self-Attention for 3D CT Diagnosis of May-Thurner Syndrome: May-Thurner Syndrome (MTS), also known as iliac vein compression syndrome or Cockett's syndrome, is a condition potentially impacting over 20 percent of the population, leading to an increased risk of iliofemoral deep venous thrombosis. In this paper, we present a 3D-based deep learning approach called MTS-Net for diagnosing May-Thurner Syndrome using CT scans. To effectively capture the spatial-temporal relationship among CT scans and emulate the clinical process of diagnosing MTS, we propose a novel attention module called the dual-enhanced positional multi-head self-attention (DEP-MHSA). The proposed DEP-MHSA reconsiders the role of positional embedding and incorporates a dual-enhanced positional embedding in both attention weights and residual connections. Further, we establish a new dataset, termed MTS-CT, consisting of 747 subjects. Experimental results demonstrate that our proposed approach achieves state-of-the-art MTS diagnosis results, and our self-attention design facilitates the spatial-temporal modeling. We believe that our DEP-MHSA is more suitable to handle CT image sequence modeling and the proposed dataset enables future research on MTS diagnosis. We make our code and dataset publicly available at: https://github.com/Nutingnon/MTS_dep_mhsa.<|reference_end|>
@article{huang2024mts-net:,
    title={MTS-Net: Dual-Enhanced Positional Multi-Head Self-Attention for 3D CT
  Diagnosis of May-Thurner Syndrome},
    author={Yixin Huang, Yiqi Jin, Ke Tao, Kaijian Xia, Jianfeng Gu, Lei Yu, Lan
  Du, Cunjian Chen},
    journal={arXiv preprint arXiv:2406.04680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04680},
    primaryClass={eess.IV cs.CV}
}