相关实验视频

Updated: Jun 27, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

在视觉变压器模型中量化解释可重现性,使用TAVACAC

Yue Zhao¹, Dylan Agyemang², Yang Liu¹

¹The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

Science advances

|December 20, 2024

概括

我们开发了训练注意力和验证注意力一致性 (TAVAC) 来检测用于医学成像的视觉变压器 (ViT) 模型中的过拟合. TAVAC准确地识别错误的预测,并提高了数字病理学中的解释可重现性.

科学领域:

人工智能的人工智能
数字病理学数字病理学
医学成像分析医学成像分析

背景情况:

深度学习,特别是视觉转换器 (ViT) 模型,显示出从生物医学图像中提取诊断特征的前景,在空间关系捕获和可解释性方面超过传统的CNN.
然而,有限的注释数据集可能导致ViT模型过拟合,导致由于噪音导致不准确的预测,阻碍临床应用.
确保人工智能模型解释的可靠性和可重现性对于临床诊断和基础科学研究都至关重要.

研究的目的:

引入训练注意力和验证注意力一致性 (TAVAC),这是一个用于评估视觉转换器 (ViT) 模型超拟合在生物医学图像分析中的新型指标.
量化ViT模型生成的解释的可重现性,确保可靠的特征提取.
在ViT模型中区分在目标和目标之外的注意力机制.

主要方法:

通过比较ViT模型的培训和测试阶段之间的高度关注区域,开发了TAVAC.
该指标在四个公共图像分类数据集和两个独立的乳腺癌组织图像数据集上得到了验证.
评估了TAVAC在目标和非目标注意力之间进行区分的能力,并在细胞水平上测量解释泛化.

主要成果:

装备过多的ViT车型与一般化的车型相比,总是表现出明显较低的TAVAC分数.
在模型中,TAVAC有效地区分了相关的 (在目标上) 和无关的 (在目标之外) 注意模式.
该指标证明了在细粒度细胞水平上测量解释概括的能力,适用于生物医学和一般图像.

结论:

TAVAC是评估ViT模型过拟合和提高数字病理学及其他领域的解释性可重现性的强大指标.
该指标有助于识别因过度拟合而产生的不可靠预测,从而提高AI在医学诊断中的可靠性.
塔瓦克的应用扩展到基础研究,通过可靠的成像数据解释,促进发现关键的空间模式和细胞结构.

更多相关视频

Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes

Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes

Published on: March 3, 2023

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

相关概念视频

Equivalent Circuits for Practical Transformers

Equivalent Circuits for Practical Transformers

The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The winding...

Three-Winding Transformers

Three-Winding Transformers

Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...

Transformers with Off-Nominal Turns Ratios

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...