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相关概念视频

The Thyroid Gland01:23

The Thyroid Gland

6.6K
The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
6.6K
Synthesis and Regulation of Thyroid Hormones01:20

Synthesis and Regulation of Thyroid Hormones

7.2K
Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...
7.2K

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相关实验视频

Updated: Jan 15, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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通过多尺度特征融合和多实例学习进行甲状腺病理图像分类.

Xiangzhi Li1,2,3, Guanxin Liu4, Mengmeng Sun1,2

  • 1Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumour, The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China.

Diagnostic pathology
|October 15, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于甲状腺癌诊断的AI工具,提高了准确性并减少了病理学家的工作量. 新的弱监督的多实例学习框架在内部和外部数据集上显示出有希望的结果.

关键词:
数字病理图像数字病理图像核聚变技术是可以实现的.多功能多功能多功能.多实例学习是指多实例的学习.多个尺度的多个尺度.

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Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
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Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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相关实验视频

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
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科学领域:

  • 计算病理学计算病理学
  • 人工智能在诊断中的应用
  • 数字病理学数字病理学

背景情况:

  • 全球甲状腺癌发病率正在上升.
  • 传统的诊断是耗时的,需要专家解释.
  • 需要有效和准确的诊断工具.

研究的目的:

  • 开发甲状腺癌的辅助诊断工具.
  • 减少病理学家的工作量,提高诊断准确度.
  • 研究一种新的多功能融合架构的有效性.

主要方法:

  • 在模型开发中使用了543个全幻灯片图像 (WSI).
  • 采用了多功能融合架构,结合了RetCCL,iBOT和DINO嵌入式.
  • 在四个多实例学习 (MIL) 框架 (CLAM-SB,CLAM-MB,DTFD,LA-MIL) 中评估了污点规范化和多尺度分析.
  • 在一个独立的128个WSI集上验证.

主要成果:

  • 污点正常化,多尺度和多特征融合显著改善了分类性能.
  • 内部数据集 (10倍CV):AUC比基线提高了2.8% (0.9900),准确度比基线提高了7.2% (0.9594).
  • 外部验证数据集:AUC 0.9584,准确度 0.9070,精度 0.9247,F1 评分 0.9348. 准确度 0.9070,精度 0.9247,F1 评分 0.9348.
  • 在两个数据集上都表现出强大可靠的性能.

结论:

  • 提出了一个弱监督的MIL框架用于甲状腺癌诊断.
  • 该方法集成了多尺度分析和跨模型特征融合.
  • 在内部和外部数据集中显示出有希望和一致的结果.
  • 潜在的协助病理学家,特别是在资源有限的环境中,等待进一步的验证.