临床超声成像的深度学习:从监督方法到基础模型
IEEE journal of biomedical and health informatics
|November 26, 2025
概括
基础模型,在庞大的数据集上进行预训练,为超声波成像中的AI提供了范式转变. 它们有望克服数据稀缺等局限性,并在各种临床应用中提高诊断性能.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 超声波技术 超声波技术 超声波技术
背景情况:
- 传统的深度学习模型缺乏跨领域的概括性.
- 基础模型,在广泛的数据上进行预训练,使广泛的任务支持成为可能.
- 超声波成像是一种低成本,多功能临床工具,具有AI增强的潜力.
研究的目的:
- 审查临床超声波中的深度学习 (DL) 应用.
- 突出基础模型在超声波诊断中的潜力.
- 讨论超声波中DL的挑战和未来方向.
主要方法:
- 对使用DL的超声波图像分析最新文献的综述.
- 强调监督,自我监督,歧视,生成和基础模型.
- 基于DL的超声波临床应用的概述.
主要成果:
- 基础模型显示了提高超声波诊断性能的前景.
- DL的进步包括监督,自我监督和生成AI方法.
- 关键的挑战包括有限的标记数据和域变性.
结论:
- 基础模型可以显著扩大超声波的范围并提高超声波的诊断准确性.
- 应对数据稀缺等挑战对于更广泛的采用至关重要.
- 未来的研究应该专注于推进临床超声波的DL技术.
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