{"title":"上消化道肿瘤的形态学和特征分析:数字病理学揭示的事实*","authors":"Prabhakaran Mathialagan, Malathy Chidambaranathan","doi":"10.1109/ICCCIS51004.2021.9397133","DOIUrl":null,"url":null,"abstract":"Upper Aero Digestive Tract cancer is treated as the primary cancer type compared to other different cancers. Exploring the morphological behaviour and characteristics of biopsy tissue sample is very significant in tumour grade analysis for proper diagnosis. After all, the manual microscopic tissue analysis process is considered as the golden standard. Traditional pathological study is still challenging and tough to overcome the manual tissue analytical barriers. To develop an efficient automated computer aided system for1. cancer tissue analysis, 2. tumour grade classification and3. survival prediction of cancer patients. The combination of different image vision techniques and microscopic image analysis tools are used to develop the state-of-the-art frameworks which will be efficient to extract different morphological features from different UADT tumours. The extracted biopsy tissue morphological features will be taken for automatic tumour grade classification that helps in assisting the pathologists to overcome the manual microscopic cancer grade classification problems. The state-of-the-art automated tissue analysis framework is developed to extract the features from the tissue samples within the short period of time. The proposed framework will be efficient for automated tissue characteristic analysis from UADT biopsy tissue samples and that can assist the pathologists to solve the inter observer variability problems.","PeriodicalId":316752,"journal":{"name":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Morphological and Characteristic Analysis of Upper Aero-Digestive Tract Tumour: Revealing Uncovered Facts in Digital Pathology*\",\"authors\":\"Prabhakaran Mathialagan, Malathy Chidambaranathan\",\"doi\":\"10.1109/ICCCIS51004.2021.9397133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Upper Aero Digestive Tract cancer is treated as the primary cancer type compared to other different cancers. Exploring the morphological behaviour and characteristics of biopsy tissue sample is very significant in tumour grade analysis for proper diagnosis. After all, the manual microscopic tissue analysis process is considered as the golden standard. Traditional pathological study is still challenging and tough to overcome the manual tissue analytical barriers. To develop an efficient automated computer aided system for1. cancer tissue analysis, 2. tumour grade classification and3. survival prediction of cancer patients. The combination of different image vision techniques and microscopic image analysis tools are used to develop the state-of-the-art frameworks which will be efficient to extract different morphological features from different UADT tumours. The extracted biopsy tissue morphological features will be taken for automatic tumour grade classification that helps in assisting the pathologists to overcome the manual microscopic cancer grade classification problems. The state-of-the-art automated tissue analysis framework is developed to extract the features from the tissue samples within the short period of time. The proposed framework will be efficient for automated tissue characteristic analysis from UADT biopsy tissue samples and that can assist the pathologists to solve the inter observer variability problems.\",\"PeriodicalId\":316752,\"journal\":{\"name\":\"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCIS51004.2021.9397133\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCIS51004.2021.9397133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Morphological and Characteristic Analysis of Upper Aero-Digestive Tract Tumour: Revealing Uncovered Facts in Digital Pathology*
Upper Aero Digestive Tract cancer is treated as the primary cancer type compared to other different cancers. Exploring the morphological behaviour and characteristics of biopsy tissue sample is very significant in tumour grade analysis for proper diagnosis. After all, the manual microscopic tissue analysis process is considered as the golden standard. Traditional pathological study is still challenging and tough to overcome the manual tissue analytical barriers. To develop an efficient automated computer aided system for1. cancer tissue analysis, 2. tumour grade classification and3. survival prediction of cancer patients. The combination of different image vision techniques and microscopic image analysis tools are used to develop the state-of-the-art frameworks which will be efficient to extract different morphological features from different UADT tumours. The extracted biopsy tissue morphological features will be taken for automatic tumour grade classification that helps in assisting the pathologists to overcome the manual microscopic cancer grade classification problems. The state-of-the-art automated tissue analysis framework is developed to extract the features from the tissue samples within the short period of time. The proposed framework will be efficient for automated tissue characteristic analysis from UADT biopsy tissue samples and that can assist the pathologists to solve the inter observer variability problems.