{"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}
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.
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.
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