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With the development of computer-aided diagnosis (CAD) and image scanningtechnology, Whole-slide Image (WSI) scanners are widely used in the field ofpathological diagnosis. Therefore, WSI analysis has become the key to moderndigital pathology. Since 2004, WSI has been used more and more in CAD. Sincemachine vision methods are usually based on semi-automatic or fully automaticcomputers, they are highly efficient and labor-saving. The combination of WSIand CAD technologies for segmentation, classification, and detection helpshistopathologists obtain more stable and quantitative analysis results, savelabor costs and improve diagnosis objectivity. This paper reviews the methodsof WSI analysis based on machine learning. Firstly, the development status ofWSI and CAD methods are introduced. Secondly, we discuss publicly available WSIdatasets and evaluation metrics for segmentation, classification, and detectiontasks. Then, the latest development of machine learning in WSI segmentation,classification, and detection are reviewed continuously. Finally, the existingmethods are studied, the applicabilities of the analysis methods are analyzed,and the application prospects of the analysis methods in this field areforecasted.

A Comprehensive Review of Computer-aided Whole-slide Image Analysis:  from Datasets to Feature Extraction, Segmentation, Classification, and  Detection Approaches