用肿瘤领域的人工智能修复人类

Ian Morilla
{"title":"用肿瘤领域的人工智能修复人类","authors":"Ian Morilla","doi":"10.35713/aic.v2.i5.60","DOIUrl":null,"url":null,"abstract":"Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale. This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular. In this work, we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer, reinforcement or federated learning. Complementary, we also introduce the recently popular method of topological data analysis that improves the performance of learning models.","PeriodicalId":415276,"journal":{"name":"WArtificial Intelligence in Cancer","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Repairing the human with artificial intelligence in oncology\",\"authors\":\"Ian Morilla\",\"doi\":\"10.35713/aic.v2.i5.60\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale. This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular. In this work, we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer, reinforcement or federated learning. Complementary, we also introduce the recently popular method of topological data analysis that improves the performance of learning models.\",\"PeriodicalId\":415276,\"journal\":{\"name\":\"WArtificial Intelligence in Cancer\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"WArtificial Intelligence in Cancer\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35713/aic.v2.i5.60\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"WArtificial Intelligence in Cancer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35713/aic.v2.i5.60","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

人工智能是一种突破性的工具,可以大规模地学习和分析从任何数据集中提取的更高特征。这种能力使它非常适合面对生物医学领域或肿瘤学领域普遍出现的任何复杂问题。在这项工作中,我们设想通过连接其他相关的子领域,如迁移、强化或联合学习,为这一数学学科的发展提供一个全局的视角。此外,我们还介绍了最近流行的拓扑数据分析方法,该方法可以提高学习模型的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Repairing the human with artificial intelligence in oncology
Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale. This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular. In this work, we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer, reinforcement or federated learning. Complementary, we also introduce the recently popular method of topological data analysis that improves the performance of learning models.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信