{"title":"放射诊断新技术","authors":"V. Chernobrivtseva, A. S. Misyurin","doi":"10.31917/2304203","DOIUrl":null,"url":null,"abstract":"Today, modern medical imaging techniques are in the process of developing and implementing machine learning and neural network-based programs to improve diagnosis of several diseases, including cancer. Such methods of image processing are superior to traditional methods of image assessment and reduce the time required and diagnostic error rate. In addition, more advanced programs, in the foreseeable future, will be able to act as a second opinion for the doctor, helping him in making decisions on the choice of techniques for treating patients. In this paper some aspects related to radiomics and radiogenomics, machine learning and the challenges of implementing the latest image evaluation algorithms are discussed.","PeriodicalId":101072,"journal":{"name":"Reports of Practical Oncology","volume":"64 12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NEW TECHNOLOGIES IN RADIOLOGY DIAGNOSTICS\",\"authors\":\"V. Chernobrivtseva, A. S. Misyurin\",\"doi\":\"10.31917/2304203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today, modern medical imaging techniques are in the process of developing and implementing machine learning and neural network-based programs to improve diagnosis of several diseases, including cancer. Such methods of image processing are superior to traditional methods of image assessment and reduce the time required and diagnostic error rate. In addition, more advanced programs, in the foreseeable future, will be able to act as a second opinion for the doctor, helping him in making decisions on the choice of techniques for treating patients. In this paper some aspects related to radiomics and radiogenomics, machine learning and the challenges of implementing the latest image evaluation algorithms are discussed.\",\"PeriodicalId\":101072,\"journal\":{\"name\":\"Reports of Practical Oncology\",\"volume\":\"64 12\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reports of Practical Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31917/2304203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reports of Practical Oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31917/2304203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Today, modern medical imaging techniques are in the process of developing and implementing machine learning and neural network-based programs to improve diagnosis of several diseases, including cancer. Such methods of image processing are superior to traditional methods of image assessment and reduce the time required and diagnostic error rate. In addition, more advanced programs, in the foreseeable future, will be able to act as a second opinion for the doctor, helping him in making decisions on the choice of techniques for treating patients. In this paper some aspects related to radiomics and radiogenomics, machine learning and the challenges of implementing the latest image evaluation algorithms are discussed.
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