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  • 《实体瘤病理数据集建设和数据标注质量控制专家共识》筹备组.实体瘤病理数据集建设和数据标注质量控制专家意见(2019)[J].第二军医大学学报,2019,40(5):465-470    [点击复制]
  • Preparatory Group for Expert Consensus on Establishment of Pathological Data Set and Quality Control of Labeling for Solid Tumor.Establishment of pathological data set and quality control of labeling for solid tumor: expert opinion 2019[J].Acad J Sec Mil Med Univ,2019,40(5):465-470   [点击复制]
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实体瘤病理数据集建设和数据标注质量控制专家意见(2019)
《实体瘤病理数据集建设和数据标注质量控制专家共识》筹备组
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摘要:
病理诊断是肿瘤诊断的金标准,是临床治疗的基石。人工智能在肿瘤组织和细胞检测方面已经取得显著进展,有助于病理医师准确、高效、定量地识别出肿瘤细胞和(或)肿瘤特征,提高工作效率,弥补病理医师短缺。发展病理人工智能的前提是高效、精准的标注工作,即将各种类型和不同分化程度的肿瘤细胞勾勒出来。为了促进行业规范性发展、加强数据标注质量控制,肿瘤学、病理学、电子信息学等领域专家共同组建了《实体瘤病理数据集建设和数据标注质量控制专家共识》筹备组,致力于推进实体瘤病理人工智能标准化数据集的建设。本文从实体瘤病理数据的标本来源、标注团队、标注规则、标注流程、质量控制、疑难病例解决方案等多个环节介绍肿瘤细胞标注过程中达成的初步意见。
关键词:  肿瘤  病理学  人工智能  数据标注  质量控制
DOI:10.16781/j.0258-879x.2019.05.0465
投稿时间:2019-04-30修订日期:2019-05-15
基金项目:
Establishment of pathological data set and quality control of labeling for solid tumor: expert opinion 2019
Preparatory Group for Expert Consensus on Establishment of Pathological Data Set and Quality Control of Labeling for Solid Tumor
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Abstract:
Pathological diagnosis is the gold standard of tumor diagnosis and the cornerstone of clinical treatment. Artificial intelligence (AI) has made significant progress in detecting tumor tissues and tumor cells, which contributes to accurately, efficiently and quantitatively identifying tumor cells and/or tumor characteristics, leading to improved efficiency of pathologists and making up for the shortage of pathologists. The premise of pathological AI is efficient and accurate labeling, which is to outline the tumor cells of various types and different degrees of differentiation. To promote the standardization and data quality control of labeling, experts of oncology, pathology, electronic information science and other fields jointly discussed the pathological data set construction and data quality control for solid tumor, and thus an expert group was formed for a future expert consensus. Our group is dedicated to the construction of the AI-based standardized pathological data set for solid tumor. This paper introduces the primary opinions reached by our group in the process of tumor cell labeling from multiple aspects, including specimen source, labeling team, labeling rules, labeling process, quality control, and solutions for difficult cases.
Key words:  neoplasms  pathology  artificial intelligence  labeling  quality control