Cancer detection research papers
Cancer detection and prevention impact factor
Introduction Pathology diagnosis has been performed by a human pathologist observing the stained specimen on the slide glass using a microscope. This is also true for pathological image analysis [  ,  ,  ]. Various local features such as gray level co-occurrence Matrix GLCM and local binary pattern LBP have been used for histopathological image analysis, but deep learning algorithms such as convolutional neural network [ 9 , 10 ,  ,  ,  ] starts the analysis from feature extraction. Abstract Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. In recent years, attempts have been made to capture the entire slide with a scanner and save it as a digital image whole slide image, WSI [ 1 ]. Elsevier would like to take this opportunity to acknowledge the significant and important contribution that ISPO has made to the scientific and medical community. The first issue to be published under Elsevier ownership is Volume 32 Issue 3. This article has been cited by other articles in PMC. Then feature extraction and classification between cancer and non-cancer are performed in each local patch. Machine Learning Methods Fig. Features and classifiers are simultaneously optimized in deep learning and features learned in deep learning often outperforms other traditional features in histopathological image analysis. Therefore, even though many techniques introduced in this review are related to deep learning, most of them are also applicable for other machine learning algorithms. The journal will publish original research articles and review articles on all aspects on cancer epidemiology, detection and prevention.
The first issue to be published under Elsevier ownership is Volume 32 Issue 3. Features and classifiers are simultaneously optimized in deep learning and features learned in deep learning often outperforms other traditional features in histopathological image analysis.
In recent years, attempts have been made to capture the entire slide with a scanner and save it as a digital image whole slide image, WSI [ 1 ].
Several reviews that have been published recently discuss histopathological image analysis including its history and details of general machine learning algorithms [  ]; in this review, we provide more pathology-oriented point of view.
In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions. Elsevier is proud to acknowledge the enormous contribution of Professor Nieburgs and his colleagues.
As a large number of WSIs are being accumulated, attempts have been made to analyze WSIs using digital image analysis based on machine learning algorithms to assist tasks including diagnosis.
The goal of feature extraction is to extract useful information for machine learning tasks.
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