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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
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基于CNN的跨模式融合,用于使用乳房影像和超声波进行增强的乳腺癌检测.

Yi-Ming Wang1, Chi-Yuan Wang2, Kuo-Ying Liu3

  • 1Department of Critical Care Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 824005, Taiwan.

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概括
此摘要是机器生成的。

一个定制的17层卷积神经网络 (CNN) 模型有效地融合了乳房影像和超声波图像,显著提高了乳腺癌检测准确度. 这种方法为更早,更可靠的诊断提供了一个有希望的工具.

关键词:
人工智能的人工智能是人工智能.乳腺癌 乳腺癌 乳腺癌卷积神经网络是一种卷积神经网络.深度学习算法深度学习算法

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 乳腺癌仍然是一个重大的全球健康问题,需要改进早期检测方法.
  • 当前的非侵入性成像技术,如乳房影像和超声波,在独立使用时,诊断准确性的局限性.
  • 结合成像模式,有可能克服个人的局限性.

研究的目的:

  • 通过整合乳房扫描和超声波成像数据来提高乳腺癌检测的准确性.
  • 开发和评估用于跨模式图像融合的先进卷积神经网络 (CNN) 架构.
  • 为了比较基于CNN的不同乳腺癌分类模型的性能.

主要方法:

  • 利用公开的乳腺成像数据集 (RSNA,PAS,Kaggle) 进行培训和验证.
  • 采用数据增强来解决超声数据中的阶级不平衡.
  • 开发并比较了三种基于CNN的方法:使用ML分类器预训练CNN,转移学习CNN和自定义的17层CNN.

主要成果:

  • 定制的17层CNN实现了最高的性能,精度为0.964和卡帕得分为0.927.
  • 转移学习CNN的结果中等 (准确率为0.846,卡帕为0.694).
  • 跨模式融合有效地利用了来自乳房影像和超声波的互补信息.

结论:

  • 定制的CNN架构与交叉模式成像相结合,显著提高了乳腺癌检测可靠性.
  • 定制设计的CNN模型为早期乳腺癌诊断提供了一个实用的解决方案.
  • 这种方法有可能减少诊断错误并改善患者的治疗结果.