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.
@article{arxiv.2201.08550,
title = {What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review},
author = {Xiaoqi Li and Haoyuan Chen and Chen Li and Md Mamunur Rahaman and Xintong Li and Jian Wu and Xiaoyan Li and Hongzan Sun and Marcin Grzegorzek},
journal= {arXiv preprint arXiv:2201.08550},
year = {2022}
}