基于深度学习的淋巴细胞检测和分类,在病理图像中增强生成形态.
Chia-Feng Juang1, Ya-Wen Chuang2, Guan-Wen Lin1
1Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan, ROC.
概括
这项研究引入了一种深度学习方法,用于在病理图像中自动检测和分类质. 这种方法提高了病理学家的诊断效率和准确性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 病理图像中的淋巴体形态对于诊断和预后至关重要.
- 目前的手动口译是耗时和劳动密集型的,需要高效,标准化的方法.
研究的目的:
- 开发和验证基于深度卷积神经网络 (CNN) 的方法,用于自动化囊检测和分类.
- 为了提高解释病理图像的效率和准确性.
主要方法:
- 用一个具有特征金字塔网络 (FX-FPN) 的平面化Xception在更快的R-CNN框架内检测淋巴细胞.
- 一个平的Xception分类器被用来分类五种球体形态.
- 使用循环一致的生成对抗网络 (CycleGAN) 来生成块补丁的数据增强.
主要成果:
- 检测模型在H&E和PAS染料中获得了0.9524的F1得分.
- 归类灵敏度和特异性分别为0.7077和0.9316,使用原始数据.
- 生成式数据增强提高了对0.7623的灵敏度和对0.9443.3的特异性.
结论:
- 拟议的深度CNN方法有效地检测和分类细胞在病理图像中.
- 生成式数据增强显著提高了分类性能,为手工解释提供了更好的替代方案.
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