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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

657
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
657
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K
Three-Compartment Open Model01:06

Three-Compartment Open Model

1.0K
The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
1.0K
Crossover Experiments01:16

Crossover Experiments

4.7K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
4.7K

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

Impact of injectable vitamin B12 among malnourished Indian children.

Bioinformation·2026
Same author

Deep learning based phase retrieval with complex beam shapes for beam shape correction.

Optics express·2025
Same author

Artesunate Perturbs GTP Binding of the Conserved GTPase Obg Thereby Alleviating Antibiotic Resistance in Methicillin-Resistant <i>Staphylococcus aureus</i>.

ACS infectious diseases·2025
Same author

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi-CSI Data.

Sensors (Basel, Switzerland)·2023
Same author

Use of Domain Labels during Pre-Training for Domain-Independent WiFi-CSI Gesture Recognition.

Sensors (Basel, Switzerland)·2023
Same author

Efficient Synergistic Antibacterial Activity of α-MSH Using Chitosan-Based Versatile Nanoconjugates.

ACS omega·2023

相关实验视频

对于3D深度学习模型的内在和后期可解释性的跨领域基准.

Asmita Chakraborty1, Gizem Karagoz1, Nirvana Meratnia1

  • 1Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.

Journal of imaging
|February 26, 2026
PubMed
概括

这项研究引入了一个统一的框架,用于对3D数据中的可解释AI (XAI) 进行基准测试. 结果显示,没有任何一个XAI方法在所有领域都表现出色,这强调了对特定领域的评估的需要.

科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 医疗成像医学成像

背景情况:

  • 对于3D数据的深度学习在医学成像,物体识别和机器人技术中很普遍.
  • 越来越多的人工智能使用需要可解释性,因为模型的黑子性质.
  • 缺乏标准化的基准标准阻碍了对3D数据可解释AI (XAI) 方法的可靠比较.

研究的目的:

  • 提出一个统一的基准测试框架,用于评估各种3D数据集上的内在和后期XAI方法.
  • 在医学CT扫描,CAD模型和点云中对各种XAI技术的性能进行定量评估.
  • 为在不同3D数据领域选择合适的XAI方法提供比较见解.

主要方法:

  • 开发了一个统一的对比框架,用于3D可解释性.
  • 在CT扫描 (MosMed),CAD模型 (ModelNet40) 和点云 (ScanObjectNN) 上评估了XAI方法 (Grad-CAM,集成梯度,突出度,阻塞,ResAttNet-3D).
  • 使用正确性 (AOPC),完整性 (AUPC) 和紧性指标评估解释质量.

主要成果:

  • 在不同方法和领域,解释质量差异很大.
  • 在医学CT扫描中,Grad-CAM和内在注意力表现最好.
关键词:
三维深度学习是什么?3D医学成像 3D医学成像3D点云分析分析 3D点云分析基准测试框架 基准测试框架可解释的人工智能 (XAI)本质的解释性是内在的.后期的可解释性.定量评估量化评价语音化的数据.

相关实验视频

  • 基于梯度的方法在基于voxelized和基于点的数据上显示出优异的性能.
  • 统计测试证实了方法之间的显著性能差异.
  • 结论:

    • 在所有3D数据领域中,没有一个XAI方法是普遍优越的.
    • 对特定领域的评估和多度量评估对于选择有效的XAI技术至关重要.
    • 拟议的框架允许对3D可解释性进行可重复和标准化的评估.