{"title":"克服医疗保健深度学习中的计算资源限制:针对数据、模型和计算的策略","authors":"Han Yuan","doi":"10.1002/med4.70001","DOIUrl":null,"url":null,"abstract":"<p>Deep learning has been identified as an indispensable backbone in health data science. However, the computational constraints faced by many healthcare providers, who may lack access to high-performance computing resources, must be considered. This commentary illustrates three representative strategies from the perspective of data, model, and computing to mitigate computational constraints in resource-limited settings.\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":100913,"journal":{"name":"Medicine Advances","volume":"3 1","pages":"42-45"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/med4.70001","citationCount":"0","resultStr":"{\"title\":\"Overcoming Computational Resource Limitations in Deep Learning for Healthcare: Strategies Targeting Data, Model, and Computing\",\"authors\":\"Han Yuan\",\"doi\":\"10.1002/med4.70001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Deep learning has been identified as an indispensable backbone in health data science. However, the computational constraints faced by many healthcare providers, who may lack access to high-performance computing resources, must be considered. This commentary illustrates three representative strategies from the perspective of data, model, and computing to mitigate computational constraints in resource-limited settings.\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":100913,\"journal\":{\"name\":\"Medicine Advances\",\"volume\":\"3 1\",\"pages\":\"42-45\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/med4.70001\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medicine Advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/med4.70001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medicine Advances","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/med4.70001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Overcoming Computational Resource Limitations in Deep Learning for Healthcare: Strategies Targeting Data, Model, and Computing
Deep learning has been identified as an indispensable backbone in health data science. However, the computational constraints faced by many healthcare providers, who may lack access to high-performance computing resources, must be considered. This commentary illustrates three representative strategies from the perspective of data, model, and computing to mitigate computational constraints in resource-limited settings.