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Computer Science > Computer Vision and Pattern Recognition

arXiv:2201.08550 (cs)
[Submitted on 21 Jan 2022 (v1), last revised 8 May 2022 (this version, v2)]

Title:What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Authors:Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, Marcin Grzegorzek
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2201.08550 [cs.CV]
  (or arXiv:2201.08550v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2201.08550

Submission history

From: Haoyuan Chen [view email]
[v1] Fri, 21 Jan 2022 05:54:14 UTC (11,355 KB)
[v2] Sun, 8 May 2022 16:19:14 UTC (10,368 KB)
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