CDP-KDNet:以课程为指导的动态修剪和知识蒸,以节省资源的超声波弹性学
Yan Li1, Tianqiang Xiang1, Jiachen Dang1
1School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, China.
Ultrasonic imaging
|December 30, 2025
概括
本研究介绍了CDP-KDNet,这是一种用于超声波运动估计的压缩深度学习模型. 它以显著更少的参数和计算实现了高精度,使其适用于资源有限的设备.
科学领域:
- 医学成像医学成像
- 机器学习 机器学习
- 超声波技术的超声波技术
背景情况:
- 基于卷积神经网络 (CNN) 的光流模型在射频 (RF) 超声波和B模式 (BM) 运动估计方面表现出色.
- 复杂的CNN架构在资源有限的设备上存在部署挑战,原因是参数数量高和计算成本高.
研究的目的:
- 开发一种新的,压缩的深度学习模型,用于超声波运动估计,可以降低复杂性,同时保持性能.
- 整合动态修剪,知识蒸和课程学习,以实现高效的模型压缩.
主要方法:
- 开发了一个教师网络 (UMEN-Net),用于无监督的运动估计.
- 创建了一个修剪的子网络 (DP-Net) 并应用压缩技术产生最终模型 (CDP-KDNet).
- 在模拟,幻影和体内超声数据上评估了CDP-KDNet.
主要成果:
- 与DP-Net和其他轻量级CNN相比,CDP-KDNet在轴向应变估计方面表现出优异的信号噪声比率 (SNR) 和对比噪声比率 (CNR).
- 该模型的性能与教师网络相当,仅使用了45.3%的参数和67.8%的浮点运算.
- 作为一个无监督的模型,CDP-KDNet消除了在培训期间需要基础真相标签的需要.
结论:
- 对于超声波运动估计,CDP-KDNet可显著降低模型复杂度.
- 该模型提供了一个有希望的,高效的,无标签的解决方案,用于在资源有限的设备上部署先进的运动估计技术.
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