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Survival Tree

Survival Tree

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...

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Updated: Jul 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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.

|October 4, 2023

概括

此摘要是机器生成的。

用于医学图像诊断的神经网络修剪可以改变模型行为,特别是在罕见疾病中. 放射科医生发现修剪模型不一致 (PIEs) 难以诊断,这表明需要谨慎部署.

科学领域:

人工智能的人工智能
医疗成像医学成像
计算机视觉计算机视觉

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

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

相关实验视频

Last Updated: Jul 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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,多标签临床数据模型的影响不太清楚.
这种差距给医疗保健中的诊断模型部署带来了风险.

结论:

这项研究是首次分析修剪对多标签CXR诊断神经网络的影响.
这些发现凸显了在临床AI部署中仔细考虑修剪的必要性.
了解修剪的影响对于安全可靠的医疗AI至关重要.