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

Observational Learning01:12

Observational Learning

795
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
795
Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Associative Learning01:27

Associative Learning

1.2K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.2K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

373
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
373

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

Depicting Characteristic Staghorn Vessels in Solitary Fibrous Tumor of the Liver With Contrast-Enhanced Ultrasound and Ultrasound Localized Microscopy: A Case Report.

The American journal of case reports·2026
Same author

Correction: Ruan et al. Comparison of Extraction, Isolation, Purification, Structural Characterization and Immunomodulatory Activity of Polysaccharides from Two Species of <i>Cistanche</i>. <i>Molecules</i> 2025, <i>30</i>, 4754.

Molecules (Basel, Switzerland)·2026
Same author

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Triptolide enhances lenvatinib sensitivity in hepatocellular carcinoma by regulating CERK-mediated sphingolipid-ferroptosis axis.

International immunopharmacology·2026
Same author

Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.

iScience·2026
Same author

Reconstructing shared visual experiences from human brain activity across individuals.

Medical image analysis·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
Same journal

SynReEM: Synapse Reconstruction via Instance Structure Encoding in Anisotropic Electron Microscopic Volumes.

IEEE transactions on medical imaging·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

996

SIB-MIL:Sparsity诱导的贝叶斯神经网络,用于在整个幻灯片图像分析上进行强大的多实例学习.

Yihang Chen, Tsai Hor Chan, Jianning Chen

    IEEE transactions on medical imaging
    |November 27, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了SIB-MIL,这是一个新的贝叶斯神经网络,用于整张幻灯片图像分析,改善癌症分类和减少预测差异. 该方法增强了在组织病理学图像分析中的稳定性和不确定性量化.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    725
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K

    相关实验视频

    Last Updated: Jan 10, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    996
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    725
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K

    科学领域:

    • 计算病理学计算病理学
    • 机器学习在组织病理学中的应用.
    • 医疗图像分析 医学图像分析

    背景情况:

    • 多个实例学习 (MIL) 对整个幻灯片病原体图像 (WSIs) 有效,但在过度匹配和不确定性量化方面存在困难.
    • 现有的贝叶斯神经网络 (BNNs) 在WSI分析中面临着不稳定的预测和在弱监督下高差异的挑战.

    研究的目的:

    • 开发一个强大的MIL方法用于WSI分析,可以减轻过拟合,并提供可靠的不确定性量化.
    • 通过引入稀缺性诱导的先行性来解决高斯BNN在WSI预测中的局限性.

    主要方法:

    • 拟议的SIB-MIL:一个度诱导的贝叶斯神经网络,集成到MIL框架中.
    • 在BNN参数之前使用马鞋来诱导稀疏性,过噪音,并管理预测方差.
    • 将该方法应用于使用WSIs进行癌症分类和亚型化任务.

    主要成果:

    • 在WSI分析中,SIB-MIL在现有的MIL网络中表现出更好的性能.
    • 该方法有效地解决了高斯斯BNN中常见的差异溢出问题.
    • 在对基因病理学图像任务的不确定性量化中实现了强大的性能.

    结论:

    • SIB-MIL提供了一种更强大,更注重不确定性的方法来分析整个幻灯片的病原体图像.
    • 在WSI分析中,稀缺性诱导的先验对于提高MIL性能和可靠性至关重要.
    • 这项工作通过为癌症诊断和亚型化提供了强大的工具来推进计算病理学.