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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Automated thyroid nodule classification in ultrasound imaging using a hybrid vision transformer and Wasserstein GAN with gradient penalty.

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EvoThy-Net:一种进化的编码解码网络,用于超声波成像中的甲状腺结节细分.

Naga Sujini Ganne1, Sivadi Balakrishna2

  • 1Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhrapradesh, India.

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|January 13, 2026
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概括

这项研究介绍了EvoThy-Net,一种使用进化算法的AI方法,可以在超声波图像中自动细分甲状腺结节. 它实现了卓越的准确性,通过高效可靠的自动化细分来改善癌症诊断.

关键词:
基于区块的网络网络.神经架构搜索神经架构搜索甲状腺结节细分的细分超声波图像中的超声波图像.

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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
  • 内分泌学 在内分泌学.

背景情况:

  • 甲状腺结节很常见,需要精确的细分来诊断癌症.
  • 手动细分是耗时且容易出现错误的.
  • 在超声波图像中自动化甲状腺结节细分 (TNS) 是由于复杂的组织结构而具有挑战性的.

研究的目的:

  • 开发一种用于超声图像中的甲状腺结节细分 (TNS) 的自动化方法.
  • 使用进化算法,优化神经网络架构用于TNS.
  • 通过注意力机制来提高细分性能.

主要方法:

  • 一种进化的神经架构搜索 (NAS) 方法,利用改进的基于教学优化 (ITLBO) 算法.
  • 开发一个编码器-解码器架构与动态网络结构优化.
  • 整合注意力阻断来提高细分精度.

主要成果:

  • 提出的EvoThy-Net方法在甲状腺结节细分方面表现出卓越的性能.
  • 对两个公共超声数据集的评估证实了该方法的有效性.
  • 该方法在TNS准确性方面表现优于现有的最先进模型.

结论:

  • EvoThy-Net为甲状腺结节细分提供了一种高效准确的自动化解决方案.
  • 进化的NAS方法成功地优化了用于医疗图像分析的网络架构.
  • 这项工作推进了人工智能在改善甲状腺癌诊断方面的潜力.