Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
https://orcid.org/0000-0003-1545-8885
Abstract Pathology image analysis is an essential procedure for clinical diagnosis of numerous diseases. To boost the accuracy and objectivity of the diagnosis, nowadays, an increasing number of intelligent systems are proposed. Among these methods, random field models play an indispensable role in improving the investigation performance. In this review, we present a comprehensive overview of pathology image analysis based on the Markov Random Fields (MRFs) and Conditional Random Fields (CRFs), which are two popular random field models. First of all, we introduce the framework of two random field models along with pathology images. Secondly, we summarize their analytical operation principle and optimization methods. Then, a thorough review of the recent articles based on MRFs and CRFs in the field of pathology is presented. Finally, we investigate the most commonly used methodologies from the related works and discuss the method migration in computer vision.
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
https://orcid.org/0000-0003-1545-8885
Abstract Pathology image analysis is an essential procedure for clinical diagnosis of numerous diseases. To boost the accuracy and objectivity of the diagnosis, nowadays, an increasing number of intelligent systems are proposed. Among these methods, random field models play an indispensable role in improving the investigation performance. In this review, we present a comprehensive overview of pathology image analysis based on the Markov Random Fields (MRFs) and Conditional Random Fields (CRFs), which are two popular random field models. First of all, we introduce the framework of two random field models along with pathology images. Secondly, we summarize their analytical operation principle and optimization methods. Then, a thorough review of the recent articles based on MRFs and CRFs in the field of pathology is presented. Finally, we investigate the most commonly used methodologies from the related works and discuss the method migration in computer vision.
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
https://orcid.org/0000-0003-1545-8885
Abstract Pathology image analysis is an essential procedure for clinical diagnosis of numerous diseases. To boost the accuracy and objectivity of the diagnosis, nowadays, an increasing number of intelligent systems are proposed. Among these methods, random field models play an indispensable role in improving the investigation performance. In this review, we present a comprehensive overview of pathology image analysis based on the Markov Random Fields (MRFs) and Conditional Random Fields (CRFs), which are two popular random field models. First of all, we introduce the framework of two random field models along with pathology images. Secondly, we summarize their analytical operation principle and optimization methods. Then, a thorough review of the recent articles based on MRFs and CRFs in the field of pathology is presented. Finally, we investigate the most commonly used methodologies from the related works and discuss the method migration in computer vision.
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis
Li, Yixin (Autor:in) / Li, Chen (Autor:in) / Li, Xiaoyan (Autor:in) / Wang, Kai (Autor:in) / Rahaman, Md Mamunur (Autor:in) / Sun, Changhao (Autor:in) / Chen, Hao (Autor:in) / Wu, Xinran (Autor:in) / Zhang, Hong (Autor:in) / Wang, Qian (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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