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相关概念视频

Survival Tree01:19

Survival Tree

89
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
89

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相关实验视频

Updated: Jul 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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修剪如何影响长尾多标签医学图像分类器?

Gregory Holste1, Ziyu Jiang2, Ajay Jaiswal1

  • 1The University of Texas at Austin, Austin, TX, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 13, 2023
PubMed
概括
此摘要是机器生成的。

对于胸部X射线诊断的神经网络修剪影响罕见疾病更多. 放射科医生发现,修剪后的模型识别了具有更多噪音和较低图像质量的具有挑战性的病例.

关键词:
胸部X射线 胸部X射线这是不平衡的失衡.长尾学习 长尾学习修剪 修剪 修剪 修剪

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科学领域:

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

背景情况:

  • 深度神经网络修剪可以减少模型大小和推断时间.
  • 修剪对长尾,多标签医疗数据模型的影响尚不清楚.
  • 这种知识缺口给临床诊断应用带来了风险.

研究的目的:

  • 分析修剪对神经网络的影响,通过胸部X射线 (CXR) 来诊断胸部疾病.
  • 根据频率和同时发生的情况,调查修剪如何影响不同的疾病.
  • 识别和评估具有挑战性的案例,其中修剪和未修剪的模型不同意.

主要方法:

  • 对两个大型CXR数据集的修剪效应的分析.
  • 在修剪模型中对类"易忘记性"的表征.
  • 鉴定剪切识别样本 (PIEs) 和与放射学家进行的人类读者研究.

主要成果:

  • 修剪不成比例地影响了不太频繁的疾病的诊断.
  • 放射科医生认为PIEs具有更多的标签噪音,图像质量较差,诊断难度更高.
  • 班级遗忘与疾病的频率和同时发生的模式相关.

结论:

  • 为CXR诊断进行深度神经网络的修剪需要仔细考虑其对罕见疾病的影响.
  • 了解模型异议 (PIEs) 对于安全部署至关重要.
  • 这项研究为临床AI的修剪效应提供了基础的见解.