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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

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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.
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基础模型以不确定性估计为基础的积极学习用于视网膜图像分类

Yilong Luo, Aidi Lin, Yuanyuan Peng

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    此摘要是机器生成的。

    本研究引入了基础模型与不确定性估计 (FMUE) 积极学习框架,以有效诊断视网膜疾病. 它显著提高了精度,加快了样本选择,克服了医学成像中的注释瓶.

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    科学领域:

    • 眼科医生 眼科 眼科
    • 医疗成像医学成像
    • 人工智能的人工智能

    背景情况:

    • 自动视网膜疾病诊断受到机器学习模型训练所需的广泛专家注释的阻碍.
    • 目前的积极学习方法在有效选择培训信息样本方面面临局限性,特别是在低数据制度中.

    研究的目的:

    • 开发和评估一个使用基础模型与不确定性估计 (FMUE) 进行有效的视网膜图像注释的积极学习框架.
    • 整合证据不确定性估计,以改善视网膜诊断中的样本选择,跨光学一致性断层扫描 (OCT) 和彩色底部摄影 (CFP) 模式.

    主要方法:

    • 开发了一个基于FMUE的积极学习框架,其中包含了用于不确定性估计的证据深度学习.
    • 使用不确定性意识分类器进行指导样本选择,以优先考虑信息数据点.
    • 在四个视网膜成像数据集上评估了框架的性能,并将其与传统的积极学习方法 (如基于的选择和贝叶斯主动学习通过分歧 (BALD)) 进行了比较.

    主要成果:

    • FMUE框架表现出优于传统方法的性能,CFP的精度提高了0.249,OCT的精度提高了0.194,只有2-4%的注释数据.
    • 在特定的环境中实现了比BALD快9倍的样本选择速度.
    • 有证据的不确定性指导导致了更平衡的类别分布和更好地识别代表性不足的视网膜疾病.

    结论:

    • 将基础模型与证据不确定性估计相结合,有效地解决了视网膜成像中的注释挑战.
    • 拟议的框架通过增强的样本选择和计算效率提供了实际的临床优势.
    • 这种方法促进了对视网膜疾病的自动诊断系统的部署.