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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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开发和评估对象检测的深度学习算法:实现卓越模型性能的关键点

Jang-Hoon Oh1, Hyug-Gi Kim1, Kyung Mi Lee2

  • 1Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea.

Korean journal of radiology
|July 5, 2023
PubMed
概括
此摘要是机器生成的。

医学成像中的深度学习显示出前景,但面临性能问题. 本研究确定了常见的深度学习问题,并提供解决方案,以提高模型准确性并减少研究人员的试错.

关键词:
数据增强数据增强深度学习是一种深度学习.深度学习的工作流程疾病子类 疾病子类疾病具有小尺寸的疾病.超参数优化超参数优化图像模式 图像模式对象检测检测对象检测对象检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 由于计算能力增加和GPU可用性增加,深度学习,特别是对象检测已经取得了显著的进步.
  • 这些技术在用于疾病检测的医学成像方面取得了显著的成就.
  • 然而,深度学习的性能可能不令人满意,需要尝试和错误来识别和修复问题.

研究的目的:

  • 突出可能导致医疗成像领域深度学习模型性能下降的潜在问题.
  • 讨论对于提高这些模型性能至关重要的因素.
  • 帮助研究人员在他们的深度学习努力中尽量减少试错.

主要方法:

  • 分析医疗成像深度学习管道中常见的陷.
  • 识别导致性能下降的因素.
  • 讨论改善模型性能的策略.

主要成果:

  • 在深度学习过程的每个步骤中确定了潜在的问题.
  • 讨论了影响模型性能的关键因素.
  • 为研究人员提供指导,以改善医学成像中的深度学习应用.

结论:

  • 了解潜在的深度学习问题对于成功的医学成像应用至关重要.
  • 解决这些因素可以显著提高模型性能,减少开发时间.
  • 这项研究为研究人员提供了一份指南,帮助他们了解医学成像中的深度学习的复杂性.