  With the development of computer-aided diagnosis (CAD) and image scanning
technology, Whole-slide Image (WSI) scanners are widely used in the field of
pathological diagnosis. Therefore, WSI analysis has become the key to modern
digital pathology. Since 2004, WSI has been used more and more in CAD. Since
machine vision methods are usually based on semi-automatic or fully automatic
computers, they are highly efficient and labor-saving. The combination of WSI
and CAD technologies for segmentation, classification, and detection helps
histopathologists obtain more stable and quantitative analysis results, save
labor costs and improve diagnosis objectivity. This paper reviews the methods
of WSI analysis based on machine learning. Firstly, the development status of
WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI
datasets and evaluation metrics for segmentation, classification, and detection
tasks. Then, the latest development of machine learning in WSI segmentation,
classification, and detection are reviewed continuously. Finally, the existing
methods are studied, the applicabilities of the analysis methods are analyzed,
and the application prospects of the analysis methods in this field are
forecasted.
