{"title":"人类设计的过滤器可能比机器学习的过滤器更好。","authors":"Gengsheng L Zeng","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Machine-learned image processing systems in medical imaging have shown better results than those obtained by traditional human-designed techniques. The success of machine learning techniques inspires humans to design better systems. The convolutional neural network (CNN) has a multi-channel architecture, which the conventional filters do not have. This paper proposes that by borrowing the multi-channel architecture, the human-designed denoising filter can have better performance than the machined-learned version. We illustrate the feasibility of this idea with a toy example in a sinogram denoising task in the area of tomography.</p>","PeriodicalId":72276,"journal":{"name":"Archives in biomedical engineering & biotechnology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080663/pdf/","citationCount":"0","resultStr":"{\"title\":\"Human-Designed Filters May Outperform Machine-Learned Filters.\",\"authors\":\"Gengsheng L Zeng\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Machine-learned image processing systems in medical imaging have shown better results than those obtained by traditional human-designed techniques. The success of machine learning techniques inspires humans to design better systems. The convolutional neural network (CNN) has a multi-channel architecture, which the conventional filters do not have. This paper proposes that by borrowing the multi-channel architecture, the human-designed denoising filter can have better performance than the machined-learned version. We illustrate the feasibility of this idea with a toy example in a sinogram denoising task in the area of tomography.</p>\",\"PeriodicalId\":72276,\"journal\":{\"name\":\"Archives in biomedical engineering & biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080663/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives in biomedical engineering & biotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives in biomedical engineering & biotechnology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Human-Designed Filters May Outperform Machine-Learned Filters.
Machine-learned image processing systems in medical imaging have shown better results than those obtained by traditional human-designed techniques. The success of machine learning techniques inspires humans to design better systems. The convolutional neural network (CNN) has a multi-channel architecture, which the conventional filters do not have. This paper proposes that by borrowing the multi-channel architecture, the human-designed denoising filter can have better performance than the machined-learned version. We illustrate the feasibility of this idea with a toy example in a sinogram denoising task in the area of tomography.