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

Paper-Based Colorimetric pH Test Strip Using Bio-Derived Dyes.

Biosensors·2025
Same author

Thermally Conductive Biopolymers in Regenerative Medicine and Oncology: A Systematic Review.

Pharmaceuticals (Basel, Switzerland)·2025
Same author

The effect of oxidative stress on the leading edge of the a-wave in retinitis pigmentosa.

Journal of theoretical biology·2025
Same author

Ivermectin as an Alternative Anticancer Agent: A Review of Its Chemical Properties and Therapeutic Potential.

Pharmaceuticals (Basel, Switzerland)·2025
Same author

Editorial on the Special Issue: "Advances in Retinal Image Processing".

Journal of imaging·2025
Same author

Biosensor for Bacterial Detection Through Color Change in Culture Medium.

Biosensors·2025

相关实验视频

Updated: Jun 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K

BraNet:基于深度学习算法进行乳房图像分类的移动应用程序.

Yuliana Jiménez-Gaona1,2,3, María José Rodríguez Álvarez4, Darwin Castillo-Malla5,4,6

  • 1Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador. ydjimenez@utpl.edu.ec.

Medical & biological engineering & computing
|May 1, 2024
PubMed
概括

移动应用程序BraNet在乳房超声波图像的分类中显示出高准确度,超过了乳房扫描的分类. 这凸显了深度学习中数据多样性对于乳腺癌检测的重要性.

关键词:
乳腺癌 乳腺癌 乳腺癌深度学习是一种深度学习.乳房学 乳房学 乳房学移动应用 移动应用超声波超声波是指超声波的使用.

更多相关视频

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

2.7K
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

相关实验视频

Last Updated: Jun 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K
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

2.7K
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

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 移动健康服务提供者

背景情况:

  • 移动健康 (mHealth) 应用程序利用人工智能进行乳腺癌检测,帮助放射科医生并减少误诊.
  • 深度学习模型越来越多地用于瘤学中的图像分析.

研究的目的:

  • 开发"BraNet",一个开源的移动应用程序,用于使用深度学习对二维乳房图像进行细分和分类.
  • 为了评估BraNet应用程序在分类乳房显影 (DM) 和超声波 (US) 乳房图像中的性能.

主要方法:

  • 使用React Native开发BraNet,包括预训练的细分 (SAM) 和分类 (ResNet18) 模型.
  • 训练有素的模型使用由SNGAN模型生成的合成图像.
  • 为iOS和Android设备实现了一个客户端-服务器架构.
  • 在290张RoI图像上与两名放射科医生进行了读者研究,使用kappa系数评估协议.

主要成果:

  • 与DM相比,BraNet在分类良性和恶性美国图像方面取得了高准确性 (94.7%/93.6%) (培训I:80.9%/76.9%;培训II:73.7%/72.3%).
  • 放射科医生的精度为DM的29%,美国的70%,在美国分类中表现更高.
  • 卡帕值表明读者之间对德国图像达成公平协议 (0.3),对美国图像达成中等协议 (0.4).

结论:

  • 该 BraNet 应用程序在分类乳房超声波图像方面表现出卓越的性能.
  • 数据的数量和多样性,特别是对具有多种BI-RADS类别的乳房造影,对于乳腺癌检测中准确的深度学习模型性能至关重要.