What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review
It addresses the problem of enhancing diagnostic accuracy in lymphoma histopathology for medical professionals, but it is incremental as it reviews existing methods.
This paper reviews the applications of machine vision algorithms, particularly deep learning, for analyzing lymphatic histopathology images to assist doctors in diagnosis, noting improvements in accuracy for tasks like segmentation, classification, and detection.
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.