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Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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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

Published on: November 30, 2022

动态权重翻译转移学习失衡的医学图像分类.

Chenglin Yu^1,2, Hailong Pei³

¹School of Electrtronic & Information Engineering and Communication Engineering, Guangzhou City University of Technology, Guangzhou 510800, China.

Entropy (Basel, Switzerland)

|May 24, 2024

概括

此摘要是机器生成的。

本研究介绍了动态权重翻译转移学习 (DTTL),以改善不平衡的医疗图像分类. DTTL有效地解决了领域转移和类不平衡,提高了诊断模型在临床环境中的性能.

关键词:

类分布类分布基于信任的选择选择.翻译周期翻译周期翻译周期动态权重的权重是动态的.不平衡的医学图像分类转移学习转移学习

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科学领域:

人工智能的人工智能
医疗成像医学成像
机器学习机器学习

背景情况:

深度学习在医学图像诊断方面表现有前途.
现实世界的应用程序面临着诸如领域转移和阶级不平衡等挑战.
这些问题导致了有偏见的模型和对新数据集的无效性能.

研究的目的:

为不平衡的医学图像分类提出一种新的转移学习解决方案.
解决医疗图像诊断中的领域转移和阶级不平衡问题.
加强深度学习模型在临床医学中的实际应用.

主要方法:

动态权重翻译转移学习 (DTTL) 框架.
模块包括跨域区分能力适应 (CDA),动态域翻译 (DDT) 和平衡目标学习 (BTL).
使用信息和理论,合成数据生成和基于信心的选择.

主要成果:

DTTL显著提高了不平衡的医疗图像分类性能.
该方法有效地缓解了域移动和阶级不平衡问题.
与现有的最先进的方法相比,在广泛的实验中获得了更高的性能.

结论:

DTTL为实际的医学图像诊断提供了强大的解决方案.
在医学成像深度学习中创新了和信息理论的应用.
在具有挑战性的临床场景中显示出更好的诊断模型准确性和可靠性.