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基于模型的数据驱动的对抗性积极学习用于脑瘤细分的细分.

Siteng Ma1, Prateek Mathur1, Zheng Ju1

  • 1School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland.

Computers in biology and medicine
|May 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的积极学习 (AL) 框架,用于医疗图像细分. 该方法显著减少了对脑MRI和瘤细分等任务的注释需求,使用更少的数据实现了具有竞争力的结果.

关键词:
积极学习是指积极学习.敌对的攻击是敌对的攻击.深度学习是一种深度学习.医疗图像细分 医疗图像细分

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科学领域:

  • 医学图像分析 医学图像分析
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉

背景情况:

  • 主动学习 (AL) 旨在通过选择信息样本来减少机器学习中的标签工作.
  • 现有的AL细分方法往往适应分类技术,忽视医学成像特点,如数据稀缺和类不平衡.
  • 医疗图像细分面临诸多挑战,包括高阶层不平衡,领域差异和有限的注释数据.

研究的目的:

  • 开发一种专门为医疗图像细分而设计的新型主动学习 (AL) 框架.
  • 解决当前AL方法在处理医疗图像特征方面的局限性.
  • 通过减少大量手动注释的需求,提高医疗图像分割的效率和有效性.

主要方法:

  • 引入了基于伪标签的过器,以处理医疗异常细分 (例如,病变,瘤) 中过多的空白补丁.
  • 提出了一种新的查询策略,将模型影响和数据稳定性结合起来,使用对抗性攻击进行样本选择.
  • 利用查询过程中生成的对抗性样本来增强模型的稳定性.

主要成果:

  • 拟议的AL框架在医疗图像细分方面与最先进的方法相比,表现出了有效性.
  • 获得了不到14%的注释贴片的竞争性结果,用于3D脑MRI多发性硬化症 (MS) 细分.
  • 获得了具有竞争力的结果,20%的注释贴片用于低级质瘤 (LGG) 瘤细分,与完全监督相比.

结论:

  • 新的AL框架显著减少了医疗图像细分任务所需的注释工作.
  • 该方法提高了模型的性能和稳定性,同时减轻了专家注释者的时间负担.
  • 这种方法通过提高效率和可访问性,促进了医疗图像细分方面的进步.