{"title":"用于高倍组织病理学显微图像分析的团聚块","authors":"Hyun-Cheol Park, Sang-Woong Lee","doi":"10.1145/3440943.3444356","DOIUrl":null,"url":null,"abstract":"The input image scale must be considered in the microsatellite instability recognition method through deep learning image analysis. Since pathological images can observe various features through high magnification, an image analysis method capable of analyzing high-resolution images is required. Although CNN has excellent image analysis capabilities, the size of input images is limited. If we want to analyze an area bigger than the input image size of the CNN, the area should be reduced or crop. In this paper, we propose a recombination block that extracts and combines features in patch units to handle microsatellite images made up of high-resolution images.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reunion Block for High Magnification Histopathology Microscopic Image Analysis\",\"authors\":\"Hyun-Cheol Park, Sang-Woong Lee\",\"doi\":\"10.1145/3440943.3444356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The input image scale must be considered in the microsatellite instability recognition method through deep learning image analysis. Since pathological images can observe various features through high magnification, an image analysis method capable of analyzing high-resolution images is required. Although CNN has excellent image analysis capabilities, the size of input images is limited. If we want to analyze an area bigger than the input image size of the CNN, the area should be reduced or crop. In this paper, we propose a recombination block that extracts and combines features in patch units to handle microsatellite images made up of high-resolution images.\",\"PeriodicalId\":310247,\"journal\":{\"name\":\"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3440943.3444356\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3440943.3444356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reunion Block for High Magnification Histopathology Microscopic Image Analysis
The input image scale must be considered in the microsatellite instability recognition method through deep learning image analysis. Since pathological images can observe various features through high magnification, an image analysis method capable of analyzing high-resolution images is required. Although CNN has excellent image analysis capabilities, the size of input images is limited. If we want to analyze an area bigger than the input image size of the CNN, the area should be reduced or crop. In this paper, we propose a recombination block that extracts and combines features in patch units to handle microsatellite images made up of high-resolution images.