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Gradient and Del Operator01:14

Gradient and Del Operator

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In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.1K
Reducing Line Loss01:18

Reducing Line Loss

197
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
197
Observational Learning01:12

Observational Learning

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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...
319
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Force Classification01:22

Force Classification

1.7K
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,...
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相关实验视频

Updated: Sep 18, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

在联合学习中,对梯度逆转攻击的影子防御.

Le Jiang1, Liyan Ma2, Guang Yang3

  • 1Bioengineering Department and Imperial-X, Imperial College London, London W12 7SL, UK.

Medical image analysis
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

联合学习 (FL) 通过保护患者数据,提高了医疗保健AI中的隐私. 一个新的框架使用影子模型来识别敏感的图像区域,从而实现有针对性的噪音注入,以防止从梯度反转攻击中侵犯隐私.

关键词:
联合学习是联合学习.梯度逆转攻击的攻击.医学图像 医学图像 医学图像隐私保护 隐私保护 隐私保护

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

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相关实验视频

Last Updated: Sep 18, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 医疗成像医学成像

背景情况:

  • 联合学习 (FL) 能够实现协作模式培训,而无需共享敏感的患者数据,这对于医疗保健隐私至关重要.
  • 梯度逆转攻击 (GIA) 通过从模型更新中重建训练数据,危及患者保密,构成重大威胁.
  • 当前对GIA的防御通常缺乏特异性,导致过度保护降低模型准确性或保护不足.

研究的目的:

  • 开发一种新的联合学习防御框架,增强对梯度反转攻击的隐私.
  • 通过识别和准敏感的图像区域来提高隐私保护的具体性.
  • 为了减轻隐私泄露,同时尽量减少对模型性能和准确性的影响.

主要方法:

  • 实施一个联合学习框架,其中包含一个可解释的影子模型.
  • 基于已识别的敏感图像区域,制定特定样本的噪音注入策略.
  • 在医学成像数据集 (ChestXRay,EyePACS) 和各种医学图像类型上进行了广泛的实验.

主要成果:

  • 拟议的防御策略在隐私保护方面取得了显著的改进,PSNR的差异为3.73,ChestXRay的SSIM差异为0.2,PSNR差异为2.78,EyePACS的SSIM差异为0.166.
  • 对模型性能的不良影响被最小化,与最先进的方法相比,F1减少不到1%.
  • 联邦平均化 (FedAvg) 观察到一致的防御改进,在各种GIA类型的LPIPS和SSIM中超过1.5%.

结论:

  • 开发的框架为医疗成像的联合学习提供了针对性和有效的防范梯度逆转攻击.
  • 基于可解释性的方法成功地平衡了隐私保护与模型实用性,优于现有的方法.
  • 该框架在不同的医疗图像类型中展示了概括性,并提供了针对各种GIA威胁的强有力的保护.