使用对抗性潜伏转换进行视觉归属
Tehseen Zia1, Abdul Wahab1, David Windridge2
1COMSATS University Islamabad, Pakistan; Medical Imaging and Diagnostics Lab, National Center of Artificial Intelligence, Pakistan.
Computers in biology and medicine
|October 1, 2023
概括
本研究介绍了使用对抗性潜伏转换 (VA2LT) 的视觉归属,用于在医学图像中精确地定位疾病指标. VA2LT有效地产生反事实的正常图像,在疾病分析中表现优于现有的方法.
科学领域:
- 医学成像分析分析 医学成像分析
- 计算机视觉 计算机视觉 计算机视觉
- 医疗保健中的人工智能
背景情况:
- 在医学图像中精确地定位疾病指标对于理解疾病进展和弱监督细分至关重要.
- 目前的方法依赖于类突出区域或对抗性图像对图像翻译,在捕获所有相关特征方面存在局限性,容易产生数据偏差或产生低效的解决方案.
研究的目的:
- 提出一种新的方法,使用对抗性潜伏转换 (VA2LT) 的视觉归属,以解决现有疾病指标定位方法的局限性.
- 在医学图像中准确地定位疾病指标,以改善疾病理解和诊断相关分析.
主要方法:
- VA2LT采用对抗式学习,通过识别和改变潜伏空间中的差异,从异常图像中生成反事实 (CF) 正常图像.
- 查询和CF潜伏表示之间的循环一致性被用来指导训练过程.
主要成果:
- 拟议的VA2LT方法在三个不同的数据集上进行了评估:合成数据集,阿尔茨海默病神经成像计划 (ADNI) 数据集和BraTS数据集.
- 与基线和相关方法相比,VA2LT在所有评估数据集中表现出优异的性能.
结论:
- VA2LT为医学成像中的视觉归属和疾病指标定位提供了更有效和更强大的方法.
- 该方法通过潜伏空间操纵生成CF正常图像的能力,显示出对推进医学图像分析和诊断的重大前景.
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