计算机科学
软件部署
深度学习
人工智能
临床实习
医学诊断
比例(比率)
前列腺癌
机器学习
病理
医学物理学
癌症
医学
内科学
地图学
地理
家庭医学
操作系统
作者
Gabriele Campanella,Matthew G. Hanna,Luke Geneslaw,Allen P. Miraflor,Vitor Werneck Krauss Silva,Klaus J. Busam,Edi Brogi,Victor E. Reuter,David S. Klimstra,Thomas J. Fuchs
出处
期刊:Nature Medicine
[Springer Nature]
日期:2019-07-15
卷期号:25 (8): 1301-1309
被引量:1691
标识
DOI:10.1038/s41591-019-0508-1
摘要
The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.
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