mlsanityData to 7 Oct 2026

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

arXiv 2201.08550 ↗First posted 21 Jan 2022cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 213 references that are in the map. First posted on arXiv 21 Jan 2022.

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16 of 213 references.

References · 16

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4 more
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  1. 2026Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Imagescited by 1