在语网络中对距离函数进行基准测试,用于当前和以前的乳房图像分析
Sahand Hamzehei1, Afsana Ahsan Jeny1, Annie Jin2
1Computer Science & Engineering, University of Connecticut, Storrs, USA.
Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|July 14, 2025
概括
一个结合辐射基函数 (RBF) 与母体共变的新型距离函数显著改善了基于人工智能 (AI) 的乳房镜分析,使用罗网络,提高了早期疾病检测的诊断准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 乳腺镜像分析对于早期发现乳腺癌至关重要.
- 人工智能 (AI),特别是语网络,在比较当前和以前的乳房造影时显示出前途.
- 在这个应用程序中,选择有效的距离函数是语网络的一个关键挑战.
研究的目的:
- 探索姆网络中非线性和关联敏感距离函数对乳房图分析的影响.
- 为了对各种距离函数进行基准测试,并引入一种新的组合来提高性能.
- 提高人工智能的诊断准确度和概括能力,在乳房影像.
主要方法:
- 实现并评估了几种距离函数:欧几里德函数,曼哈顿函数,马哈拉诺比斯函数,辐射基函数 (RBF) 和等号函数.
- 引入并测试了一种新的距离函数:RBF与母体共变相结合.
- 使用准确度,灵敏度,精度,特异性,F1得分和AUC等指标对配的乳房镜像进行基准性能测试.
主要成果:
- 具有母性共变距离函数的RBF始终优于传统函数.
- 具有拟议的距离函数的ResNet50模型实现了高性能指标 (例如,精度0.938,AUC0.940).
- 该方法在30个交叉验证样本中显示出强度和通用性.
结论:
- 非线性和基于关联的距离函数对于乳房图分析中有效的语网络性能至关重要.
- 具有母体共变的RBF提供了一种优越的方法,用于捕捉相关的乳房图像中的微妙差异.
- 这项研究推进了人工智能驱动的乳房学,可能导致更准确,更可靠的诊断工具.
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