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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

525
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
525

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相关实验视频

Updated: Jun 7, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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用未标记数据进行瘤预测的不确定性估计.

Juyoung Yun1, Shahira Abousamra1, Chen Li1

  • 1Stony Brook University, Department of Computer Science, USA.

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops
|November 18, 2024
PubMed
概括

本研究引入了数字病理学模型的新学习方法,以更好地估计使用未标记数据的神经网络的不确定性. 该方法通过有效利用现有数据来提高模型的透明度和可信度.

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科学领域:

  • 数字病理学数字病理学
  • 机器学习 机器学习
  • 不确定性量化不确定性的量化.

背景情况:

  • 估计神经网络的不确定性对于人工智能模型的透明度和可信度至关重要.
  • 数字病理学产生了大量未标记的数据,这对模型培训构成了挑战.
  • 现有的方法可能无法充分利用未标记的数据来估计不确定性.

研究的目的:

  • 开发一种用于数字病理学预测模型中不确定性估计的新型学习方法.
  • 在数字病理学中有效利用大型未标记数据集.
  • 提高AI模型在该领域的可信度和透明度.

主要方法:

  • 提出了一种新的学习方法,旨在利用未标记的数据.
  • 将该方法应用于数字病理学预测任务.
  • 与基线方法进行性能比较,包括蒙特卡洛退出.

主要成果:

  • 与现有的基线相比,拟议的方法显示出更高的性能.
  • 对不确定区域的分析为模型行为提供了洞察力.
  • 这种方法提高了数字病理学模型的可靠性.

结论:

  • 这种新型的学习方法有效地利用未标记的数据用于数字病理学的不确定性估计.
  • 这种方法提高了模型的透明度和可信度.
  • 对模型不确定性的进一步检查可以产生有价值的见解.