一种新的多维联合搜索方法,用于医学图像分割模型的压缩
Yunhui Zheng1, Zhiyong Wu1, Fengna Ji1
1School of Computer Science and Technology, Shandong University of Technology, Zibo 255049, China.
Journal of imaging
|September 27, 2024
概括
本研究介绍了一种有效的搜索策略,用于创建较小的变压器网络,用于医疗图像细分. 该方法将模型大小减少30%,同时提高准确性,使人工智能更容易获得医疗保健.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 变压器在计算机视觉任务中表现出强的表现.
- 它们在医疗图像细分中的应用越来越多.
- 然而,变压器导致模型参数大,增加计算资源需求和培训时间.
研究的目的:
- 探索灵活高效的搜索策略,以确定连续变压器网络中的最佳子网.
- 为了应对大型模型参数和与医疗图像细分中的变压器相关的高计算成本的挑战.
主要方法:
- 一个可学习和统一的L1稀疏性约束被用来指导寻找高效的子网.
- 搜索策略包括反映搜索空间不同维度全球重要性的因素.
- 集成了一个像素分类模块,以减轻在子网搜索过程中的潜在准确性损失.
主要成果:
- 提出的方法在模型参数和FLOP (每秒浮点运算) 中实现了30%的压缩.
- 该模型在自动心脏诊断挑战 (ACDC) 数据集上的准确性略有改善.
- 搜索过程是高效的,只需要一轮的培训.
结论:
- 开发的策略有效地减少了用于医疗图像细分的变压器模型的计算负担.
- 这种方法提高了在资源有限的环境中使用变压器的实用性.
- 该方法在医学成像应用中提供了模型效率和诊断精度之间的平衡.
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