提高乳房摄影:对提高图像质量的计算机方法进行全面审查
Joana Cristo Santos1, Miriam Seoane Santos2,3, Pedro Henriques Abreu1
1University of Coimbra, CISUC, Department of Informatics Engineering, Coimbra 3030-290, Portugal.
Progress in biomedical engineering (Bristol, England)
|December 10, 2024
概括
机器学习技术显著提高了乳房扫描图像质量,以更好地检测乳腺癌. 解决噪音和数据集的局限性是未来诊断准确性的关键.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
背景情况:
- 乳腺扫描对于乳腺癌检测至关重要,但面临着图像质量的挑战.
- 图像质量差可能导致误诊,辐射增加和成本增加.
研究的目的:
- 审查传统和机器学习方法用于乳房图像增强.
- 确定有效的技术来提高诊断准确度和减少错误.
主要方法:
- 关于对比度增强和降噪的115篇文章的文献搜索 (2015-2024年).
- 评估传统方法 (直方图等级,过) 和机器学习 (ML) 方法.
- 专注于机器学习架构,比如用卷积神经网络 (CNN) 排斥自编码器.
主要成果:
- 机器学习,特别是自编码器-CNN混合体,有效地提高了乳房镜像的图像质量,而不会损失细节.
- 经过审查的技术在改善乳房图像的视觉质量方面取得了成功.
- 确定的持续挑战包括高噪音,不一致的指标和有限的数据集.
结论:
- 基于ML的图像增强为改善乳房影像提供了巨大的潜力.
- 需要进一步的研究来克服目前的局限性,并推进该领域.
- 解决噪音问题,标准化指标和扩大数据集将提高临床效用.
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