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相关概念视频

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Types Of Transformers01:16

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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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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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
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从图像到序列:探索视觉转换器用于光学一致性断层学分类.

Amirali Arbab1, Aref Habibi1, Hossein Rabbani2

  • 1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.

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概括
此摘要是机器生成的。

结合CNN和视觉变压器 (ViT) 的新混合型号在光学连贯断层扫描 (OCT) 图像分类中实现了99.80%的准确性. 这种高效的模型需要较少的参数,因此非常适合用于视网膜疾病诊断的临床应用.

关键词:
计算机视觉 计算机视觉 计算机视觉卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.多头自我注意的多头自我注意.光学连贯性断层扫描 (optical coherence tomography) 是一种光学连贯性断层扫描技术.视觉变压器 视觉变压器

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 光学连贯断层扫描 (OCT) 对于诊断视网膜疾病至关重要,如糖尿病性黄斑胀和与年龄相关的黄斑变性.
  • 这些情况给全球健康带来了重大挑战,如果不及早发现,可能导致视力丧失.
  • 目前的OCT图像分类方法由于复杂的视网膜结构和数据集可变性而面临挑战.

研究的目的:

  • 开发一种新型混合模型,用于增强海外国家和地区的图像分类.
  • 为了解决当前的OCT分析方法的局限性.
  • 提高诊断关键视网膜疾病的准确性和效率.

主要方法:

  • 集成卷积神经网络 (CNN) 用于局部特征提取和视觉变换器 (ViT) 用于远程模式识别.
  • 开发一种混合模式,利用CNN和ViT的互补优势.
  • 混合模型应用于OCT图像数据集进行分类.

主要成果:

  • 混合模型在OCT2017数据集上实现了99.80%的高精度.
  • 该模型表现出显著的参数效率,仅使用690万个参数.
  • 这种效率超过了像Xception和OpticNet-71.1这样的大型模型的效率.

结论:

  • 开发的混合CNN-ViT模型为OCT图像分类提供了高度准确和参数高效的解决方案.
  • 它的效率使其适用于有限的计算资源的临床环境.
  • 这一进步支持对关键视网膜疾病的快速和必要的诊断.