用于高倍组织病理学显微图像分析的团聚块

Hyun-Cheol Park, Sang-Woong Lee
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引用次数: 0

摘要

基于深度学习图像分析的微卫星不稳定性识别方法必须考虑输入图像的尺度。由于病理图像可以通过高倍放大观察到各种特征,因此需要一种能够分析高分辨率图像的图像分析方法。虽然CNN具有出色的图像分析能力,但是输入图像的大小是有限的。如果我们想要分析比CNN输入图像尺寸更大的区域,则应该减少或裁剪该区域。在本文中,我们提出了一种提取和组合斑块单元特征的重组块来处理由高分辨率图像组成的微卫星图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reunion Block for High Magnification Histopathology Microscopic Image Analysis
The input image scale must be considered in the microsatellite instability recognition method through deep learning image analysis. Since pathological images can observe various features through high magnification, an image analysis method capable of analyzing high-resolution images is required. Although CNN has excellent image analysis capabilities, the size of input images is limited. If we want to analyze an area bigger than the input image size of the CNN, the area should be reduced or crop. In this paper, we propose a recombination block that extracts and combines features in patch units to handle microsatellite images made up of high-resolution images.
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