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在毒性研究中对深度学习算法用于检测肝脏亡的实施进行比较研究

Ji-Hee Hwang¹, Minyoung Lim¹, Gyeongjin Han¹

¹Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea.

Toxicological research

|July 3, 2023

概括

像DeepLabV3+和Mask R-CNN这样的细分算法在分析数字病理学图像中的异常病变方面表现出高准确度. 这些方法优于在非临床研究中用于病理分析的物体检测.

科学领域:

数字病理学数字病理学
计算病理学计算病理学
毒理学成像成像研究

背景情况:

深度学习越来越多地用于分析非临床研究中的数字病理图像.
评估用于异常病变分析的深度学习算法的比较研究有限.

研究的目的:

为了比较三个深度学习算法 (SSD,Mask R-CNN,DeepLabV3+) 在组织幻灯片图像中检测肝脏缩的性能.
确定最有效的深度学习算法,用于在非临床环境中对异常病变的病理分析.

主要方法:

应用了三种深度学习算法 (SSD,Mask R-CNN,DeepLabV3+) 来检测肝硬化.
算法被训练在5750个图像上,有5835个注释,并增加了500个图像.
在60张测试图像上,使用精度,回忆和准确度来评估性能.

主要成果:

DeepLabV3+实现了0.94准确度和最高回忆率.
面具R-CNN实现了0.92的准确性.
与细分算法相比,对象检测算法SSD的准确性较低.

结论:

分段算法 (DeepLabV3+,Mask R-CNN) 在非临床研究中更适合于对象检测算法,用于对异常病变的病理分析.
精确的局部化和病变的分离对于幻灯片级别的调查至关重要.

关键词:

深度学习是一种深度学习.肝脏亡 - 肝脏亡组织病理学组织病理学图像分析图像分析毒理学毒理学毒理学

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