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作 者:陈青[1] 郭广秀[1] 易云[2] 朱贤鑫[1] CHEN Qing;GUO Guang-xiu;YI Yun;ZHU Xian-xin(Department of Pathology,Ganzhou People’s Hospital,Jiangxi Ganzhou 341000;College of Mathematics and Computer Science,Gannan Normal University,Jiangxi Ganzhou 341000)
机构地区:[1]赣州市人民医院病理科,江西赣州341000 [2]赣南师范大学数学与计算机科学学院,江西赣州341000
出 处:《中国医疗器械信息》2024年第21期54-57,共4页China Medical Device Information
基 金:江西省卫生健康委科技计划项目(项目名称:基于自监督学习的尿路上皮癌辅助筛查研究,项目编号:202410880);江西省自然科学基金面上项目(项目名称:基于智能监控腔内压力与CTC在非肌层膀胱癌血行转移中的研究,项目编号:20224BAB206084);赣州市卫生健康委指导性科研计划项目(项目名称:基于上下文表征学习的尿路上皮癌辅助筛查研究,项目编号:GZWJW202402115)。
摘 要:目的:为提高尿路上皮癌的筛查效率和准确性,基于人工智能和病理学理论,设计尿路上皮癌辅助筛查系统。方法:为获得高质量的细胞学图片,使用薄层自动细胞制片机,设计基于高敏薄层的尿脱落细胞学检测方法。以该方法为基础,提出基于自监督学习的尿路上皮癌目标检测算法。结果:与传统细胞学方法获得的细胞图像比较,该方法获得的细胞图像更清晰,便于观察和分析细胞核和细胞质的结构;细胞呈单层排列,分散更均匀,重叠更少;细胞学特征更接近自然状态,便于观察细胞形态和异常变化。结论:在尿路上皮癌辅助筛查任务中,该系统提高了临床病理医生的工作效率。Objective:In order to improve the screening efficiency and accuracy of urothelial carcinoma,an auxiliary screening system for urothelial carcinoma was designed based on artificial intelligence and pathological theory.Methods:In order to obtain high quality cytological images,a thin-layer automatic cell production machine was used to design a highly sensitive thin-layer cytological detection method for urine shedding.Based on this method,a self-supervised learning target detection algorithm for urothelial carcinoma was proposed.Results:Compared with the cell images obtained by traditional cytology methods,the cell images obtained by this method are clearer and easier to observe and analyze the structure of the nucleus and cytoplasm.The cells are arranged in a single layer and dispersed more evenly,with less overlapping.The cytological features are closer to their natural state,making it easier to observe the cell morphology and abnormal changes.Conclusion:This system can improve the efficiency of clinical pathologists in the task of auxiliary screening for urothelial carcinoma.
关 键 词:尿路上皮癌 自监督学习 尿脱落细胞学 多重掩码 上下文
分 类 号:TH776[机械工程—仪器科学与技术]
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