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通过无监督域适应方法在显微镜和放大器上增强医疗图像分析

Talha Ilyas¹, Khubaib Ahmad¹, Dewa Made Sri Arsa²

¹Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, Republic of Korea; Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, 54896, Republic of Korea.

Computers in biology and medicine

|January 31, 2024

概括

富里埃适应性识别系统 (FARS) 通过提高检测疟疾和癌症等疾病的适应性来增强医疗图像分析. 这种深度学习模型实现了更好的交叉放大和跨域性能,提升了诊断能力.

科学领域:

医疗图像分析医学图像分析
深度学习是一种深度学习.
计算病理学计算病理学

背景情况:

深度学习模型在医学图像分析方面表现出色,但在各种成像条件和放大度中难以保持稳定性.
现有的方法通常依赖于界限框标签,限制了对显微镜幻灯片的详细分析.

研究的目的:

引入福里埃适应性识别系统 (FARS) 以提高医学图像分析的适应性,最初用于疟疾寄生虫识别.
展示FARS在更广泛的应用领域的潜力,包括瘤和癌症诊断.
为了提高跨多种显微镜配置的特征提取一致性,并增强跨领域的多功能性.

主要方法:

从界限框标签过渡到语义细分,以进行精细的幻灯片检查.
整合对抗训练和颜色域意识里埃域适应 (F2DA) 进行一致的特征提取.
采用类别依赖的上下文注意力来扩大跨领域的多功能性.

主要成果:

在交叉放大性能方面实现了显著的提升,从 31.3% mAP 到 55.19% mAP.
证明了对疟疾寄生虫识别的跨领域适应性有15.68%的提升.
展示了基础原则对瘤和癌症成像的适用性.

结论:

在疟疾寄生虫识别方面,FARS代表了显著的进步,提供了更好的适应性和性能.
FARS的核心方法提供了一个蓝图,用于提高瘤和癌症成像的精度.
该模型的适应性使其成为各种医学图像分析任务的宝贵工具.

关键词:

卷积神经网络是一种卷积神经网络.疟疾:疟疾是一种疾病.医学成像医学成像显微镜的使用方法分段化分段化分段化分段化不同的放大效果.

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