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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Immunocytochemistry and Immunohistochemistry01:22

Immunocytochemistry and Immunohistochemistry

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Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
These...
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相关实验视频

Updated: May 21, 2025

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
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Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

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基于内容的组织病理图像检索

Camilo Nuñez-Fernández1, Humberto Farias2, Mauricio Solar1

  • 1Departamento de Informática, Universidad Tecnica Federico Santa Maria, Campus San Joaquin, Santiago 8940897, Chile.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的局部-全球特征融合嵌入模型,以改进基因病理图像检索. 该模型通过整合多尺度信息来增强特征描述符,在化学路径24C数据集上实现99.40%的回忆@1.

关键词:
基于内容的图像检索.功能嵌入 功能嵌入 嵌入.功能融合功能融合功能组织病理学图像 组织病理学图像转移学习转移学习

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Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
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Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

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Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
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Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

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相关实验视频

Last Updated: May 21, 2025

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科学领域:

  • 数字病理学数字病理学
  • 计算机视觉 计算机视觉
  • 医学图像分析 医学图像分析

背景情况:

  • 基于内容的图像检索 (CBIR) 系统对于病理学家来说至关重要,但在组织病理学图像中提取特征描述符仍然是一个挑战.
  • 当前的深度学习模型往往错过了丰富的空间上下文,因为它们专注于单一尺度的特征.

研究的目的:

  • 开发一种改进的方法来提取在基因病理图像中的特性描述符.
  • 提高CBIR系统在数字病理学中的嵌入的深度和实用性.

主要方法:

  • 提出了局部-全球特征融合嵌入模型,结合了多尺度特征提取骨干.
  • 实现了局部-全球特征融合的子分支和基于通用平均值 (GeM) 的聚合头.
  • 在ImageNet-1k和PanNuke数据集上训练模型,使用亚中心ArcFace损失.

主要成果:

  • 拟议的模型在化学路径24C数据集上实现了99.40%的RECALL@1,用于基因病理图像检索.
  • 与最新的方法相比,在补丁级检索中表现出优越的性能.

结论:

  • 当地-全球特征融合嵌入模型有效地整合了多个尺度的信息,以进行增强的组织病理图像分析.
  • 这种方法显著提高了CBIR系统在数字病理学中的准确性.