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

Classification of Systems-II01:31

Classification of Systems-II

460
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
460
Classification of Systems-I01:26

Classification of Systems-I

552
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
552
Aggregates Classification01:29

Aggregates Classification

970
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
970
Methods of Classification and Identification01:28

Methods of Classification and Identification

1.0K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
1.0K
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K
Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.3K

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

UHPLC/Q-TOF-MS-based blood-component profiling and multi-omics analysis reveal potential protective mechanisms of Shenzhuo Formula against diabetic kidney disease.

Journal of pharmaceutical and biomedical analysis·2026
Same author

Probing Stage Transition Kinetics in Li-Graphite Intercalation Compounds by Time-Resolved In Situ Solid-State NMR via <sup>13</sup>C Labeling.

Journal of the American Chemical Society·2026
Same author

CDC20 promotes prostate cancer progression via modulating c-MYC and PI3K-AKT signaling.

iScience·2026
Same author

Three-year monitoring study of heavy metal fluxes and accumulation characteristics in mildly contaminated farmland soils of northern Guangdong, China.

Scientific reports·2026
Same author

Enzymatic preparation of luteolin-rich olive (Olea europaea L.) leaf extract and its inhibitory effects on colitis.

Bioorganic chemistry·2026
Same author

Association of Chronic Diseases With Herpes Zoster in China: A Nationwide Population-Based Survey.

Health care science·2026

相关实验视频

Updated: Jan 17, 2026

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

2.5K

DGR-MIL:探索多个实例学习中的多元全球代表性,用于整个幻灯片图像分类.

Wenhui Zhu1, Xiwen Chen2, Peijie Qiu3

  • 1Arizona State University, AZ, USA.

Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision
|September 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了整个幻灯片图像分类的多元全球表示多个实例学习 (DGR-MIL). DGR-MIL有效地模拟了实例多样性,在瘤检测中表现优于现有的方法.

关键词:
组织学整体幻灯片图像图像多个实例的学习.变压器变压器变压器弱监督的学习学习.

更多相关视频

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

1.0K

相关实验视频

Last Updated: Jan 17, 2026

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

2.5K
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

1.0K

科学领域:

  • 计算病理学计算病理学
  • 机器学习 机器学习
  • 缺乏监督的学习学习.

背景情况:

  • 多重实例学习 (MIL) 对于瘤检测中的全幻灯片图像 (WSI) 分类至关重要.
  • 目前的MIL方法主要模拟实例相关性,忽视实例多样性,导致性能不足最佳,计算成本高.

研究的目的:

  • 提出一种新的MIL聚合方法,即多元全球表示MIL (DGR-MIL),可以有效地模拟实例多样性.
  • 用MIL在WSIs中提高瘤病变检测的准确性和效率.

主要方法:

  • 开发了DGR-MIL,一种使用全球向量来表示实例多样性的聚合方法.
  • 采用交叉注意力机制来建模实例-全球向量相似性,反映实例相关性.
  • 引入了正实例对齐和基于确定点过程的多样化学习范式,以增强全球矢量表示.

主要成果:

  • DGR-MIL显著优于最先进的MIL聚合模型.
  • 在CAMELYON-16和TCGA-肺癌数据集上取得了卓越的性能.
  • 证明了对实例多样性的改进建模,以便更好地进行WSI分类.

结论:

  • 在计算病理学中,DGR-MIL为弱监督学习提供了一种强大的新方法.
  • 该方法有效地捕捉了实例多样性,从而提高了WSI中的瘤检测.
  • 拟议的技术为MIL提供了有效和理论上健全的多样化学习.