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CoSev:在低样本制度中进行COVID-19严重性评估的数据驱动优化

Aksh Garg1, Shray Alag1, Dominique Duncan2

  • 1Computer Science Department, Stanford University, Stanford, CA 94305, USA.

Diagnostics (Basel, Switzerland)
|February 10, 2024
PubMed
概括

本研究介绍了一种代转移学习方法,用于使用3DCT扫描进行COVID-19严重性分析. CoSev模型实现了81.57%的准确性,为具有挑战性的医疗图像数据集推进了计算机视觉.

关键词:
在 COVID-19 疫情中,计算机视觉 计算机视觉数据驱动的数据驱动.高维度的低样本学习学习.严重性的评估评估严重性的评估.

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