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积极学习用于左心室细分在心声回声学中的左心室细分

Eman Alajrami¹, Tiffany Ng², Jevgeni Jevsikov³

¹Intelligent Sensing and Vision, University of West London, London, UK.

Computer methods and programs in biomedicine

|March 13, 2024

概括

积极学习显著减少了对心声回声学深度学习的注释需求. 我们的方法实现了99%的性能,使用80%更少的数据,降低了成本,提高了细分效率.

科学领域:

医疗成像医学成像
深度学习 (Deep Learning) 是一种深度学习.
计算心脏病学计算心脏病学

背景情况:

医疗图像细分的深度学习需要广泛的注释数据集.
创建这些数据集是昂贵和耗时的.
积极学习通过选择信息样本提供解决方案.

研究的目的:

在心声学中研究主动学习,以有效地对左心室进行细分.
减少稀疏的专家注释的负担.
引入和评估一个新的抽样策略.

主要方法:

适应和评估各种主动学习采样技术.
引入优化代表性抽样 (ORS),将异常值和代表性样本结合起来.
将方法应用于具有稀疏注释的心声回声数据集.

主要成果:

使用仅20%的标记数据实现了99%的性能,减少了1680张图像的注释.
在公共数据集上,ORS 显示了 70% 的注释量减少,超过了基线主动学习 (50% 的减少).
在抽样策略中突出显示了数据集特定的性能变化.

结论:

开发了一种具有成本效益的积极学习方法,用于心声回声学细分.
公开发布了一套新的数据集,以推进有效的医学图像注释研究.
为了解医疗图像细分中的高效注释策略做出了贡献.

关键词:

积极学习是指积极学习.深度学习是一种深度学习.心声回声图 (Echocardiography) 是一种心声回声图.图像细分图像细分图像细分

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