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

Serotonin 5-HT7 receptor signaling modulates inflammatory responses and survival after myocardial infarction.

Journal of translational medicine·2026
Same author

Investigating Structurally and Pigmentary Colored Featherworks via Noninvasive Methodologies.

ACS omega·2026
Same author

Surgeons' perceptions of artificial intelligence (AI) for gaze guidance in laparoscopic cholecystectomy.

Surgical endoscopy·2026
Same author

Describing What Surgeons See: Visual Orientation Strategies in Laparoscopic Cholecystectomy Across Imaging Conditions.

Journal of surgical education·2026
Same author

Macrophage-specific circular RNA circHIPK2, inflammation, and fibrosis after myocardial infarction.

European heart journal·2026
Same author

Optimizing atrial fibrillation detection through ECG feature selection using Extra-Trees and statistical association measures.

Journal of electrocardiology·2026
Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

385

针对图像分类的组织病理学专注的积极学习.

Arne Schmidt1, Pablo Morales-Álvarez2, Lee Ad Cooper3

  • 1Department of Computer Science and Artificial Intelligence, University of Granada, Granada, 18010, Spain.

Medical image analysis
|April 9, 2024
PubMed
概括
此摘要是机器生成的。

集中式主动学习 (FocAL) 通过使用贝叶斯神经网络和分布外检测来改善数字病理学的数据采集. 这种方法有效地选择了信息图像,优于前列腺癌分类的现有方法.

关键词:
积极学习是指积极学习.贝叶斯深度学习是贝叶斯的深度学习.癌症的分类 癌症的分类组织病理图像 组织病理图像

更多相关视频

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.0K
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.4K

相关实验视频

Last Updated: Jun 28, 2025

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

385
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.0K
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.4K

科学领域:

  • 数字病理学数字病理学
  • 机器学习 机器学习
  • 医疗成像医学成像

背景情况:

  • 有效地获取标记数据对于数字病理学中的机器学习至关重要.
  • 现有的积极学习 (AL) 方法在医疗数据中扎着文物,模糊性和阶级不平衡.
  • 缺乏精确的不确定性估计导致获得低价值图像.

研究的目的:

  • 开发一种积极学习 (AL) 方法,有效地获取数字病理学的信息图像.
  • 为了应对医疗数据集中的文物,模糊性和阶级不平衡所带来的挑战.
  • 提高机器学习模型在医学图像分析中的效率和性能.

主要方法:

  • 拟议的集中主动学习 (FocAL),集成贝叶斯神经网络与分布外检测.
  • FocAL估计了加权的认识不确定性 (对于类不平衡),随机不确定性 (对于模两可),以及分布外得分 (对于文物).
  • 在MNIST和前列腺癌分类的真实世界熊猫数据集上得到验证.

主要成果:

  • FocAL有效地专注于信息图像,避免模两可和阻碍其他AL方法的工件.
  • 在使用仅0.69%的标记数据的Panda数据集上获得0.764的Cohen's kappa.
  • 在这两种实验环境中,其表现优于现有的积极学习方法.

结论:

  • FocAL通过准确估计图像采集的多种不确定性类型来增强主动学习.
  • 该方法在具有挑战性的医学成像场景中展示了卓越的性能和数据效率.
  • FocAL为数字病理学数据的高效标记提供了一个强大的解决方案.