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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

适应组合用于半监督的医疗图像细分.

Zhiqiang Shen¹, Peng Cao², Junming Su¹

¹School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, 110819, China.

Medical image analysis

|November 22, 2025

概括

适应混合 (AdaMix) 通过在训练期间通过适应性调整图像干扰来改善半监督学习. 这种自动步进的方法增强了一致性规范化,以便在医疗图像细分任务中更好地实现模型性能.

关键词:

医疗图像细分医疗图像细分混搭 - - 混搭 - - 混搭自己节奏的学习学习.半监督学习半监督学习

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科学领域:

* 机器学习 * 机器学习
* 计算机视觉计算机视觉
* 医学图像分析

背景情况:

*混合对于半监督学习 (SSL) 的一致性规范化至关重要,产生用于伪监督的扰乱样本.
*目前的混杂方法 (随机或固定的规则) 由于不受控制或微不足道的干扰,限制了有效性.
* 需要适应性扰动来优化SSL性能.

研究的目的:

* 在SSL.培训期间调查自适应图像混杂扰动.
* 提出一个自适应混合 (AdaMix) 算法,使用自律学习策略.
* 开发和评估基于AdaMix的半监督医疗图像细分框架.

主要方法:

* 推出了AdaMix,这是一个自动学习算法,用于自适应的图像混合.
* 实施了一种自律的课程,以控制基于模型学习状态的扰乱困难.
* 开发了三个框架:AdaMix-ST,AdaMix-MT和AdaMix-CT用于医疗图像细分.

主要成果:

*AdaMix框架在3个数据集的2D和3D医疗图像细分任务中实现了卓越的性能.
*AdaMix-CT显示显著改善:在ACDC数据集上,子相似度系数为2.62%,平均表面距离为48.25% (10%标记数据).
*动态调整的混合扰动增强了一致性调整的有效性.

结论:

* AdaMix 能够为半监督学习提供有效的适应性扰动.
* 拟议的自动步进方法优化了混合,以改善医疗图像细分.
*AdaMix为提高SSL一致性规范化提供了一个有希望的方向.