Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology
Author:
Funder
Bundesministerium für Wirtschaft und Energie
Österreichische Forschungsförderungsgesellschaft
Helmholtz Association
Publisher
Elsevier BV
Subject
Pathology and Forensic Medicine
Link
https://www.nature.com/articles/s41379-022-01147-y.pdf
Reference138 articles.
1. Translational AI and deep learning in diagnostic pathology;Serag;Front Med,2019
2. Computational pathology definitions, best practices, and recommendations for regulatory guidance: A white paper from the digital pathology association;Abels;J Pathol,2019
3. Artificial intelligence in pathology: An overview;Moxley-Wyles;Diagn Histopathol,2020
4. Deep learning in cancer pathology: A new generation of clinical biomarkers;Echle;Br J Cancer,2021
5. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning;Coudray;Nat Med,2018
Cited by 35 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative;Journal of Pathology Informatics;2024-12
2. Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review;Journal of Pathology Informatics;2024-12
3. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities;Journal of Pathology Informatics;2024-12
4. Deep Learning‐Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis;Journal of Oral Pathology & Medicine;2024-09-10
5. Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer;Journal of Pathology and Translational Medicine;2024-08-09
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3