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相关概念视频

2D NMR: Homonuclear Correlation Spectroscopy (COSY)01:06

2D NMR: Homonuclear Correlation Spectroscopy (COSY)

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Homonuclear correlation spectroscopy, or COSY, is a 2-dimensional NMR technique that provides information about coupled protons. Typically, the geminal and vicinal coupling are observed. For example, consider the COSY spectrum of ethyl acetate, where its 1D proton NMR spectrum is plotted along the vertical and horizontal axes with their corresponding chemical shift scale. Three spots on the diagonal corresponding to the three peaks in the 1D proton spectrum are called diagonal peaks. The COSY...
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2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

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Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
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相关实验视频

Updated: Jul 5, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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对于内在可解释的CNN和视觉转换器的B-Cos对齐.

Moritz Bohle, Navdeeppal Singh, Mario Fritz

    IEEE transactions on pattern analysis and machine intelligence
    |January 17, 2024
    PubMed
    概括
    此摘要是机器生成的。

    我们引入了一个新的B-cos转换,通过对准权重与输入来提高深度神经网络 (DNN) 的解释性. 这种方法增强了特征突出显示和模型理解,而不会牺牲准确性.

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    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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    科学领域:

    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 深度神经网络 (DNN) 通常缺乏可解释性.
    • 了解DNN中的决策过程对于信任和调试至关重要.

    研究的目的:

    • 开发一种新的方法来提高深度神经网络 (DNN) 的解释性.
    • 在DNN培训期间促进权重输入对齐.

    主要方法:

    • 提出了一个新的B-cos转换来取代DNN中的标准线性转换.
    • 证明了一系列B-cos转换简化为单个可解释的线性转换.
    • 集成B-cos转换到各种最先进的计算机视觉架构 (ResNets,DenseNets,ConvNeXt,视觉转换器) 中.

    主要成果:

    • B-cos转换促进了重量输入对齐,导致高度可解释的诱导线性转换.
    • 这些转变有效地突出了与任务相关的特征.
    • 将其集成到现有模型中,在ImageNet.Net上保持了高精度.
    • 生成的解释显示出高视觉质量和强大的定量解释性指标.

    结论:

    • B-cos转换为提高DNN解释性提供了一个有希望的方向.
    • 它与当前架构兼容,并增强功能相关性可视化.
    • 这种方法提供了可解释但准确的深度学习模型.