结构化散列与深度学习的模式,器官和疾病内容敏感的医疗图像检索
Asim Manna1, Dipayan Dewan2, Debdoot Sheet3
1Department of Artificial Intelligence, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India. asimmanna17@kgpian.iitkgp.ac.in.
Scientific reports
|March 15, 2025
概括
MODHash通过启用基于用户偏好的语义相似性搜索来改进基于内容的医疗图像检索 (CBMIR). 这种深度神经散列方法可以从大型医疗图像档案中提取证据.
科学领域:
- 医疗成像医学成像
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 基于证据的医学依赖于从广泛的医学图像档案中有效地检索数据.
- 基于内容的医学图像检索 (CBMIR) 系统对于提取临床证据至关重要.
- 现有的深度神经哈希 (DNH) 方法需要对医疗图像的语义复杂性进行增强.
研究的目的:
- 开发一种基于结构的新哈希技术,MODHash,以改进CBMIR.
- 为了使医疗图像的检索基于模式,器官和疾病等特征的语义相似性.
- 解决当前DNH方法在处理异质医学图像数据方面的局限性.
主要方法:
- 提出了MODHash,这是CBMIR的基于结构的哈希技术.
- 使用特征特定分类损失和考奇交叉损失训练了MODHash网络.
- 在从Kaggle,Mendeley和Figshare编制的放射学数据集上评估了性能.
主要成果:
- 与最先进的方法相比,MODHash的平均精度 (mAP) 提高了12%.
- 对于前100名的检索,MODHash的正常化折扣累积收益 (NDCG) 提高了2%.
- 特定特征的检索性能证实了MODHash在与用户偏好保持一致方面的有效性.
结论:
- MODHash是一种有效的深度神经哈希方法,用于基于内容的医疗图像检索.
- 拟议的技术成功地根据用户定义的语义相似性来检索图像.
- MODHash在检索准确性和临床应用中的相关性方面提供了显著的改进.
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