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Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

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相关实验视频

Updated: Jul 15, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

CoInNet:一个卷积-卷积网络,具有用于自动聚体细分的新型统计注意力.

Samir Jain, Rohan Atale, Anubhav Gupta

IEEE transactions on medical imaging

|September 28, 2023

概括

CoInNet精确地细分微小的息肉,即使是图像区域的0.01%. 这种深度学习模型提高了胃肠道异常的早期诊断,优于现有的方法.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

胃肠多是常见的,早期诊断有助于降低结肠直肠癌风险.
自动聚细分系统帮助外科医生,但由于聚变异性和不清楚的边界,他们面临着挑战.
现有的深度学习模型 (CNN,视觉转换器) 在捕捉各种视觉模式和特征依赖性方面存在局限性.

研究的目的:

提出 CoInNet,一种新的多胞体细分模型.
为了利用卷积和卷积运算进行增强的特征提取.
为了提高多片细分的准确性,特别是对于小而容易被忽视的多片.

主要方法:

开发了CoInNet,一种具有新特征提取机制的聚体细分模型.
集成的卷积和卷积操作以捕捉不同的视觉模式.
引入了一个统计特征注意力单元来学习特征地图之间的关系.
整合了一个异常边界近似模块,具有递归特征融合,以改进细分.

主要成果:

CoInNet精确地对聚体进行细分,包括占据图像面积仅为0.01%的微小聚体.
该模型在突出聚区域和学习准确的边界方面表现出卓越的性能.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

相关实验视频

Last Updated: Jul 15, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

在5个基准聚细分数据集上,CoInNet的表现优于13种最先进的方法.

结论:

CoInNet 在自动化多重体细分方面取得了重大进展.
该模型检测小息肉的能力对于临床应用至关重要,特别是在分析大型数据集时,如无线囊内镜视频.
CoInNet显示了改善早期诊断和降低结肠直肠癌风险的巨大潜力.