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相关概念视频

Basic Discrete Time Signals01:16

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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...
Mason's Rule01:20

Mason's Rule

Mason's rule is a powerful tool in control systems and signal processing. It simplifies the calculation of transfer functions from signal-flow graphs. This method leverages various elements, including loop gains, forward-path gains, and non-touching loops, to determine the transfer function efficiently.
Loop gain is determined by identifying and tracing a path from a node back to itself. This involves computing the product of branch gains along the loop. Each loop's gain is crucial for further...

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Updated: Jun 19, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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通过内核密度矩阵进行可解释的弱监督学习:数字病理学使用案例

Sebastian Medina1,2, Eduardo Romero3, Angel Cruz-Roa4

  • 1MindLab Research Group, Universidad Nacional de Colombia, Bogotá, Colombia.

PloS one
|November 5, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了WiSDoM,这是一个新的深度学习框架,使用内核密度矩阵来统一弱监督和完全监督的分类. 它增强了在组织病理学图像分析中的解释性和不确定性量化.

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科学领域:

  • 计算病理学计算病理学
  • 机器学习是机器学习.
  • 深度学习是一种深度学习.

背景情况:

  • 深度学习分类在不确定性量化和可解释性方面面临挑战,使用完全监督与弱监督的方法.
  • 需要为两种监督模式提供一个统一的框架,并提供可量化的解释指标.

研究的目的:

  • 引入WiSDoM (弱监督密度矩阵),一个新的框架,统一完全监督和弱监督的分类.
  • 在单一模型中实现可量化的解释指标和不确定性量化.

主要方法:

  • 利用内核矩阵来建模数据和标签的概率分布.
  • 集成可微分的内核密度矩阵用于优化,局部-全球关注特征加权,并通过内核空间采样生成原型.
  • 通过密度矩阵运算用于分类的顺序回归.

主要成果:

  • 在监督补丁分类 (AUC = 0.896) 和低监督整片分类 (AUC = 0.930) 中获得了高性能.
  • 生成后面的概率分布,基于差异的不确定性地图和可解释的现象样本原型.
  • 在各监督模式的格里森分级任务中表现一致 (AUC>0.89) 和高专家一致 (0.88).

结论:

  • 核心密度矩阵为分类模型提供了坚实的基础,需要解释性和不确定性量化.
  • WiSDoM成功地统一了不同的监督模式,为计算病理学提供了更深入的见解.
  • 该框架的成果,包括不确定性图和原型,提高了模型的透明度和临床实用性.