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对乳腺癌的不同超声波方法的深度学习,从诊断到预后:当前趋势,挑战和分析
Humayra Afrin1, Nicholas B Larson2, Mostafa Fatemi1
1Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA.
Cancers
|June 28, 2023
概括
深度学习神经网络显示出使用超声波 (美国) 改善乳腺癌诊断的前景. 研究表明,在病变分类方面具有很高的性能,有助于早期发现和预后,特别是在服务不足的地区.
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:
- 乳腺癌是全球女性死亡的主要原因之一.
- 超声波 (美国) 是乳腺病变诊断和预后监测的关键非侵入性工具.
- 美国的高运营商依赖性导致虚假负面和延迟管理,特别是在服务不足的地区.
研究的目的:
- 审查乳腺质量超声波分析神经网络的最新研究趋势.
- 为各种诊断和预后任务确定最佳的超声波模式和神经网络模型.
- 突出深度学习在乳房超声波中的表现,超出了简单的诊断.
主要方法:
- 对乳房超声波神经网络的原始研究进行系统审查.
- 分析超声波模式,神经网络模型及其应用 (诊断,预后).
- 在不同任务中评估模型性能.
主要成果:
- 神经网络在乳腺损伤分类方面表现出很高的表现.
- 与诊断相比,更少的研究专注于预测预后.
- 特定的超声波模式和网络架构在某些应用中显示出优异的结果.
结论:
- 深度学习神经网络提供了一个潜在的解决方案,以提高乳房超声波解释的准确性和可访问性.
- 需要进一步的研究来提高预后能力和解决局限性.
- 人工智能驱动的乳房超声波可以促进更早,更准确的临床决策,特别是在资源有限的环境中.
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