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相关概念视频

Renal Corpuscle01:20

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The glomerulus and Bowman's capsule are two essential components of the nephron, which is the functional unit of the kidney. These microscopic structures play a critical role in the process of blood filtration to produce urine.
Glomerulus: Structure and Function
The glomerulus is a tiny, intricate network of capillaries located at the beginning of the nephron. It's enveloped by the Bowman's capsule and receives its blood supply from an afferent arteriole, which divides into numerous...
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在病理学中的机器学习.

Matthew Nicholas Basso1, Moumita Barua2,3,4,5, Julien Meyer6

  • 1Image Analysis in Medicine Lab (IAMLAB), Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON, Canada.

Frontiers in nephrology
|September 7, 2023
PubMed
概括
此摘要是机器生成的。

计算病理学模型显示出通过活检诊断脏疾病的前景. 机器学习和深度学习方法取得了显著的准确性,可能提高病理评估的效率.

关键词:
计算病理学计算病理学深度学习是一种深度学习.数字图像分析分析数字图像分析这是一种质球体.机器学习是机器学习.膜性脏病是一种膜性脏病.疾病变化最小的疾病变化.薄底层膜神经病变 - 薄底层膜神经病变

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 脏病理学 脏病理学

背景情况:

  • 传统的活检评估方法 (光显微镜,免疫光,电子显微镜) 是耗时的,昂贵的,容易变化.
  • 计算方法为查和诊断提供了潜在的解决方案,旨在减少病理学家的工作量并确定新的生物标志物.

研究的目的:

  • 评估生物标志物特征提取 (BFE) 模型和三种深度学习模型 (VGG16,VGG19,InceptionV3) 的诊断性能,用于仅使用PAS染色数字病理图像诊断淋巴细胞疾病.

主要方法:

  • BFE模型提取了233个特征,并将其减少到10个形态和纹理特征,用于使用线性差异分析进行分类.
  • 三个预训练的深度学习模型 (VGG16,VGG19,InceptionV3) 被应用于数字病理图像.
  • 数据增强和Grad-CAM被用于深度学习模型,以提高性能和可解释性.
  • 这项研究包括从最小变化疾病,膜性脏病和薄底膜脏病中进行的45次脏活检 (371个质细胞).

主要成果:

  • 该BFE模型实现了球体检测准确率为76.8%.
  • 深度学习模型显示了更高的验证准确性,VGG16达到78.5%.
  • 最高的测试准确度是VGG16在质水平 (71.9%) 和InceptionV3在患者水平 (73.3%).

结论:

  • 传统的机器学习 (BFE) 和深度学习方法都显示出有助于脏活检评估的潜力.
  • 这些计算方法可以为球细胞疾病提供高效和准确的诊断工具.