  In the past ten years, the computing power of machine vision (MV) has been
continuously improved, and image analysis algorithms have developed rapidly. At
the same time, histopathological slices can be stored as digital images.
Therefore, MV algorithms can provide doctors with diagnostic references. In
particular, the continuous improvement of deep learning algorithms has further
improved the accuracy of MV in disease detection and diagnosis. This paper
reviews the applications of image processing technology based on MV in lymphoma
histopathological images in recent years, including segmentation,
classification and detection. Finally, the current methods are analyzed, some
more potential methods are proposed, and further prospects are made.
