Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The endoplasmic reticulum is a target organelle for trivalent dimethylarsinic acid (DMAIII)-induced cytotoxicity.

Toxicology and applied pharmacology·2012
Same author

(E)-1-{4-[Bis(4-bromo-phen-yl)meth-yl]piperazin-1-yl}-3-(4-eth-oxy-phen-yl)prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

(E)-1-{4-[Bis(4-bromo-phen-yl)meth-yl]piperazin-1-yl}-3-(4-methyl-phen-yl)prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

(E)-3-(1,3-Benzodioxol-5-yl)-1-{4-[bis-(4-meth-oxy-phen-yl)meth-yl]piperazin-1-yl}prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

Economic evaluation of first-line treatments for metastatic renal cell carcinoma: a cost-effectiveness analysis in a health resource-limited setting.

PloS one·2012
Same author

Metabolism studies of casticin in rats using HPLC-ESI-MS(n).

Biomedical chromatography : BMC·2012

相关实验视频

Updated: Jul 17, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

开发和验证一种深度学习算法,用于从病理部分对子宫癌的基于模式的分类系统.

Wei Tian1,2, Siyuan Sun3, Bin Wu4

  • 1Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.

Heliyon
|September 4, 2023
PubMed
概括

深度学习系统 (DLS) 帮助病理学家使用Silva对宫内腺癌 (EAC) 进行分类.

关键词:
深度学习系统深度学习系统宫内内腺癌 宫内内腺癌 宫内内腺癌 宫内腺癌根据silva的基于模式的分类系统.在ResNet50中使用ResNet50整个幻灯片图像的图像.

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

相关实验视频

Last Updated: Jul 17, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

科学领域:

  • 在瘤学瘤学.
  • 病理学 病理学 病理学
  • 人工智能的人工智能

背景情况:

  • 席尔瓦的基于模式的分类系统 (SPBC) 改善了宫内腺癌 (EAC) 的临床预后和管理.
  • 病理学家经验的变化可能导致SPBC应用中的不一致性.
  • 需要标准化工具来提高SPBC在EAC诊断和治疗中的临床实用性.

研究的目的:

  • 开发和验证一个深度学习系统 (DLS) 用于标准化的Silva基于模式的分类 (SPBC) 在EAC.
  • 在准确分类 EAC 模式时评估 DLS 的性能.

主要方法:

  • 共有90名EAC患者被纳入,其中63名在培训组,27名在验证组.
  • 一个深度学习系统 (DLS),特别是ResNet50,被用来创建和验证SPBC的预测模型.
  • 计算了接收器运行特征曲线 (AUC) 下的面积,以评估模型性能.

主要成果:

  • 在Silva模式分类中,ResNet50实现了74.36%的整体准确性.
  • 对A,B和C模式的具体准确率分别为63.64%,55.56%和89.47%.
  • 在测试组中,ResNet50实现了AUC值:A型的0.69,B型的0.58,C型的0.91.

结论:

  • 成功建立了SPBC的DLS.
  • 开发的DLS显示出有潜力帮助病理学家准确地对EAC进行分类.
  • 这种人工智能工具可以帮助标准化SPBC,提高诊断一致性.