数字病理学中的深度学习应用

IF 28.6 1区医学 Q1 UROLOGY & NEPHROLOGY

Nature Reviews Nephrology Pub Date : 2024-07-16 DOI:10.1038/s41581-024-00870-w

Peter Boor

{"title":"数字病理学中的深度学习应用","authors":"Peter Boor","doi":"10.1038/s41581-024-00870-w","DOIUrl":null,"url":null,"abstract":"Deep Learning (DL) holds great promise to improve patient outcomes by improving the precision and speed of disease diagnosis and treatment recommendations. Given the efficacy of DL in image analysis, pathology will likely be one of the first medical fields transformed by DL. However, several challenges must be overcome before we can expect to see the use of DL transform the digital future of pathology.","PeriodicalId":19059,"journal":{"name":"Nature Reviews Nephrology","volume":"20 11","pages":"702-703"},"PeriodicalIF":28.6000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning applications in digital pathology\",\"authors\":\"Peter Boor\",\"doi\":\"10.1038/s41581-024-00870-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep Learning (DL) holds great promise to improve patient outcomes by improving the precision and speed of disease diagnosis and treatment recommendations. Given the efficacy of DL in image analysis, pathology will likely be one of the first medical fields transformed by DL. However, several challenges must be overcome before we can expect to see the use of DL transform the digital future of pathology.\",\"PeriodicalId\":19059,\"journal\":{\"name\":\"Nature Reviews Nephrology\",\"volume\":\"20 11\",\"pages\":\"702-703\"},\"PeriodicalIF\":28.6000,\"publicationDate\":\"2024-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Nephrology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.nature.com/articles/s41581-024-00870-w\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Nephrology","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41581-024-00870-w","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}

引用次数: 0

摘要

通过提高疾病诊断和治疗建议的精确度和速度，深度学习（DL）有望改善患者的治疗效果。鉴于深度学习在图像分析方面的功效，病理学很可能成为首批被深度学习改造的医学领域之一。然而，在我们期待看到使用 DL 改变病理学的数字化未来之前，必须克服几个挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Deep learning applications in digital pathology

Deep Learning (DL) holds great promise to improve patient outcomes by improving the precision and speed of disease diagnosis and treatment recommendations. Given the efficacy of DL in image analysis, pathology will likely be one of the first medical fields transformed by DL. However, several challenges must be overcome before we can expect to see the use of DL transform the digital future of pathology.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Nature Reviews Nephrology 医学-泌尿学与肾脏学

CiteScore

39.00

自引率

1.20%

发文量

127

审稿时长

6-12 weeks

期刊介绍： Nature Reviews Nephrology aims to be the premier source of reviews and commentaries for the scientific communities it serves. It strives to publish authoritative, accessible articles. Articles are enhanced with clearly understandable figures, tables, and other display items. Nature Reviews Nephrology publishes Research Highlights, News & Views, Comments, Reviews, Perspectives, and Consensus Statements. The content is relevant to nephrologists and basic science researchers. The broad scope of the journal ensures that the work reaches the widest possible audience.