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相关概念视频

Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

49.1K
Calculating pH for Titration Solutions: Weak Acid/Strong Base
For the titration of 25.00 mL of 0.100 M CH3CO2H with 0.100 M NaOH, the reaction can be represented as:
49.1K
Titration of a Weak Acid with a Strong Base01:30

Titration of a Weak Acid with a Strong Base

4.3K
In titrating a weak acid with a strong base, different calculation methods are applied at various stages. Initially, the pH of a weak acid like acetic acid is calculated using its dissociation constant (Ka) and an ICE table. Upon addition of a strong base such as sodium hydroxide, a buffer forms, and its pH is determined using the Henderson-Hasselbalch equation. As more base is added and the titration reaches the halfway point, the pH becomes equal to the pKa of the acid, indicating equal...
4.3K
Titration of a Weak Base with a Strong Acid01:20

Titration of a Weak Base with a Strong Acid

8.6K
The titration curve of a weak base like ammonia with a strong acid like hydrochloric acid is the mirror image of the titration curve of a weak acid with a strong base.
Using the ICE table and substituting the Kb value, we calculate the initial pH of 50 mL of 0.1 M ammonia to be 11.11. Addition of 25 mL of 0.1 M hydrochloric acid to this solution of ammonia results in a buffer with an equal concentration of ammonia and ammonium ions. The pH of this buffer can be calculated by substituting these...
8.6K
Weak Base Solutions03:21

Weak Base Solutions

24.9K
Some compounds produce hydroxide ions when dissolved by chemically reacting with water molecules. In all cases, these compounds react only partially and so are classified as weak bases. These types of compounds are also abundant in nature and important commodities in various technologies. For example, global production of the weak base ammonia is typically well over 100 metric tons annually, being widely used as an agricultural fertilizer, a raw material for chemical synthesis of other...
24.9K
Titration Calculations: Strong Acid - Strong Base02:28

Titration Calculations: Strong Acid - Strong Base

33.8K
Calculating pH for Titration Solutions: Strong Acid/Strong Base
A titration is carried out for 25.00 mL of 0.100 M HCl (strong acid) with 0.100 M of a strong base NaOH. The pH at different volumes of added base solution can be calculated as follows:
(a) Titrant volume = 0 mL. The solution pH is due to the acid ionization of HCl. Because this is a strong acid, the ionization is complete and the hydronium ion molarity is 0.100 M. The pH of the solution is then:
33.8K
Weak Acid Solutions04:02

Weak Acid Solutions

42.3K
Few compounds act as strong acids. A far greater number of compounds behave as weak acids and only partially react with water, leaving a large majority of dissolved molecules in their original form and generating a relatively small amount of hydronium ions. Weak acids are commonly encountered in nature, being the substances partly responsible for the tangy taste of citrus fruits, the stinging sensation of insect bites, and the unpleasant smells associated with body odor. A familiar example of a...
42.3K

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High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
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通过弱到强的概括来提高手工制作的特征导向组织学图像分类的深度学习可解释性.

Zong Fan1, Changjie Lu1, Jialin Yue2

  • 1University of Illinois Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States.

Journal of medical imaging (Bellingham, Wash.)
|January 22, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一个弱到强的概括 (WSG) 框架,以改进用于组织学图像分析的深度学习 (DL) 模型. 整合手工制作的特征 (HCF) 提高了DL模型的解释性和预测性,用于临床采用.

关键词:
深度学习功能建模深度学习功能建模功能可解释性 功能可解释性手工制作的特征建模手工制作特征建模组织学整个幻灯片图像分类图像分类.从弱到强的概括.

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科学领域:

  • 计算病理学计算病理学
  • 医学中的人工智能
  • 图像分析 图像分析

背景情况:

  • 深度学习 (DL) 模型在组织学图像分析方面表现出色,但缺乏解释性.
  • 手工制作的特征 (HCF) 提供可解释性,但具有较低的预测能力.
  • DL和HCF之间的关系未得到充分研究,阻碍了临床采用.

研究的目的:

  • 为了提高DL模型的解释性和性能在组织学图像分析.
  • 将HCF集成到DL模型中,使用弱至强泛化 (WSG) 框架.
  • 为了更好的临床采用,探索DL和HCF之间的相关性.

主要方法:

  • 开发了一个WSG框架,一个基于HCF的可解释的"弱"教师模型监督一个"强"DL学生模型.
  • 设计了一个自适应式启动WSG丢失函数,以优化从HCF到DL功能的知识传输.
  • 分析了HCF和DL特征之间的相互信息 (MI),以评估可解释性和相关性.

主要成果:

  • WSG框架在各种模型中始终改善了分类性能.
  • Saliency-map分析显示,WSG监督改善了对诊断相关区域的模型重点.
  • 定量分析显示,在WSG培训后的HCF和DL特征之间增加了MI.

结论:

  • WSG框架有效地将HCF整合到DL培训中,提高可解释性和预测性.
  • 在组织学图像分类中阐明了驱动DL预测的关键HCF.
  • 这些发现支持在病理学中更广泛地采用可解释的DL模型.