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

Artificial intelligence based assessment of treatment response in wet age related macular degeneration using paired OCT angiography.

Scientific reports·2026
Same author

Universal Vessel Segmentation for Multi-Modality Retinal Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Pointwise Structure-Function Analysis of the Ellipsoid Zone in Retinitis Pigmentosa Using an Artificial Intelligence-Assisted OCT and Microperimetry Overlay.

Ophthalmology science·2025
Same author

A Dataset for Understanding Radiologist-Artificial Intelligence Collaboration.

Scientific data·2025
Same author

OCTA-based AMD Stage Grading Enhancement via Class-Conditioned Style Transfer.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Profile of snakebite cases admitted to the Poison Control Center of Bach Mai Hospital in northern Vietnam from 2008 to 2020.

Transactions of the Royal Society of Tropical Medicine and Hygiene·2025

相关实验视频

Updated: May 24, 2025

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

2.6K

有效的培训中的自适应性化合物损失功能贡献控制用于医疗图像分割.

Abdullah F Al-Battal, Soan T M Duong, Chanh D Tr Nguyen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种适应方法,以改进基于深度学习的医学图像细分. 它有效地平衡损失功能,减少手动调节的需要,节省时间和能源.

    更多相关视频

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.2K
    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.5K

    相关实验视频

    Last Updated: May 24, 2025

    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

    2.6K
    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.2K
    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    42.5K

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 计算机视觉 计算机视觉

    背景情况:

    • 图像细分对于疾病诊断和监测等临床应用至关重要.
    • 深度神经网络是对细分的最先进的技术,但面临着诸如阶级不平衡等挑战.
    • 复合损失函数,结合二进制交叉 (BCE) 和子损失,是常见的,但需要广泛的调整.

    研究的目的:

    • 开发一种有效的训练深度神经网络的方法,用于图像细分.
    • 为了应对医学图像细分中的阶级不平衡的挑战.
    • 为了消除复合损失函数中繁的超参数微调的需要.

    主要方法:

    • 提出了一种适应性方法,在训练过程中动态控制单个损失函数的贡献.
    • 集成的二进制交叉 (BCE) 和 Dice 损失适应性.
    • 专注于提高细分模型精度和回忆,而无需手动超参数优化.

    主要成果:

    • 适应性方法消除了对多次微调代的需求.
    • 更有效地实现了对细分模型的预期精度和回忆.
    • 减少与模型培训相关的时间和能源消耗.

    结论:

    • 拟议的自适应性损失功能控制是医疗图像细分中的类不平衡的有效解决方案.
    • 这种方法简化了深度学习模型的培训过程.
    • 能够使临床图像分析工具的开发更容易获得和资源效率更高.