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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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Updated: Jul 18, 2025

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使用新型深度学习系统对高风险甲状腺结节进行分层.

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  • 1Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan.

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概括
此摘要是机器生成的。

与ResNeSt50模型相比,Swin变压器 (Swin-T) 人工智能模型显著提高了从超声波图像中分类甲状腺结节的准确性. 这种人工智能工具有助于更好的诊断和共同决策,用于甲状腺结节管理.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 目前基于超声波的甲状腺结节分类是主观的,耗时的.
  • 人工智能 (AI) 在提高甲状腺结节恶性瘤预测的准确性方面表现有前途.
  • 斯温变压器 (Swin-T) 是用于图像分类的最先进的人工智能模型.

研究的目的:

  • 评估Swin变压器模型在使用超声波图像对甲状腺结节进行分类时的有效性.
  • 为了比较Swin-T与ResNeSt50模型对甲状腺结节分类的性能.

主要方法:

  • 从139个恶性和235个良性甲状腺结节中收集超声波图像的前景采集 (2016年1月至2021年6月).
  • 使用Swin-T和ResNeSt50AI模型对甲状腺结节的分类.
  • 使用灵敏度,特异性,接收器操作特征 (ROC) 分析和麦克纳马测试进行性能评估.

主要成果:

  • 与ResNeSt50 (72.51%和77.14%) 相比,Swin-T实现了更高的平均灵敏度 (82.46%) 和特异性 (84.29%).
  • 在Swin-T的研究中,曲线下的面积 (AUC=0.91) 与ResNeSt50 (AUC=0.82) 相比较高.
  • 麦克纳马尔测试证实了Swin-T的显著更好的表现.

结论:

  • 斯温变压器模型提供了一种更准确,更可靠的方法来从超声波图像中分类甲状腺结节.
  • Swin-T可以作为一个有价值的工具,以支持医生和患者之间关于甲状腺结节管理的共同决策.
  • 这种AI方法对具有高风险超声波特征的结节特别有益.