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Updated: Jun 29, 2026

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从使用深度学习网络的2D超声波图像中自动分类结节

Tewele W Tareke1, Sarah Leclerc1, Catherine Vuillemin2

  • 1ICMUB Laboratory, UMR CNRS 6302, University of Burgundy, 7 Bld Jeanne d'Arc, 21000 Dijon, France.

Journal of imaging
|August 28, 2024
PubMed
概括

这项研究引入了一种使用深度学习的AI系统,从超声波图像中对甲状腺结节进行分类,旨在通过准确识别需要手术的结节来减少不必要的细针吸收 (FNA).

关键词:
贝塞斯达得分得分 贝塞斯达得分在DenseNet中,使用的是DenseNet.这是Grad-CAM.这是分类分类的分类.精细的针吸入精细的针.甲状腺结节 甲状腺结节超声波图像 超声波图像 超声波图像

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Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...