应用深度学习卷积神经网络识别小鼠胃鳞状细胞癌
Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice
林志 1任禹珂 1赵永田 2杨艳伟 1李双星 1屈哲 1霍桂桃 1耿兴超 1张頔1
作者信息
- 1. 中国食品药品检定研究院,国家药物安全评价监测中心,北京市重点实验室,北京 100176
- 2. 美国Indica数字病理实验室,新墨西哥州 87114
- 折叠
摘要
目的 拟通过深度学习技术,建立小鼠胃鳞状细胞癌辅助诊断模型,以提高病理诊断的准确性和一致性.方法 收集致癌性研究中小鼠胃鳞状细胞癌组织93例和正常小鼠胃组织56例,扫描成数字切片后,进行半自动化数据标注.对所有数据进行组织提取、伪影去除以及良性上皮区域剔除等预处理后,按照8∶1∶1的比例随机分为训练集、验证集和测试集.基于HALOAI平台构建DenseNet算法模型用以识别胃鳞状细胞癌区域和非鳞状细胞癌区域.采用精确率(Pr)、召回率(Re)及F1-Score对构建的算法模型进行性能评估.结果 构建的DenseNet算法模型在测试集中的总体Pr为0.904,召回率为0.929,F1-Score为0.916.结论 建立的DenseNet算法模型对于辅助诊断小鼠胃鳞状细胞癌具有良好的应用前景.
Abstract
Objective To establish a assisted diagnosis model for mouse gastric squamous cell carcinoma,by implementing deep learning technology to improve the accuracy and consistency of pathological diagnosis.Methods A total of 93 cases of gastric squamous cell carcinoma tissue and 56 cases of normal mouse gastric tissue were collected form a carcinogenicity study.After scanning into digital slide images,semi-automated data annotation was performed.After preprocessing all data with tissues detection,artifact removal,and benign epithelial region removal,they were randomly divided into training set,validation set,and test set at a ratio of 8∶1∶1.Construct a DenseNet algorithm model based on the HALO AI platform to identify areas of gastric squamous cell carcinoma and non-squamous cell carcinoma.Evaluate the performance of the constructed algorithm model using precision,recall,and F1-score.Results The overall accuracy,recall and F1 score of the DenseNet algorithm model in the test set were 0.904,0.929 and 0.916,respectively.Conclusion The DenseNet algorithm model established in this study has good application prospects for assisting diagnosis of gastric squamous cell carcinoma in mouse.
关键词
非临床药物安全性评价/毒性病理学/人工智能/深度学习/胃鳞状细胞癌Key words
non-clinical safety evaluation of drug/toxicologic pathology/artificial intelligence/deep learning/gastric squamous cell carcinoma引用本文复制引用
基金项目
中检院学科带头人课题(2021X2)
药品监管科学全国重点实验室课题(2023SKLDRS0127)
出版年
2024
NETL
NSTL
万方数据