增量学习与转移学习相结合:应用于多部位前列腺MRI细分的应用
Chenyu You1, Jinlin Xiang2, Kun Su2
1Electrical Engineering, Yale University, New Haven, CT, USA.
Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : Third MICCAI Workshop, DeCaF 2022 and Second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Sin...
|July 7, 2023
概括
我们介绍了增量转移学习 (ITL),这是一个新的框架,用于在多个数据集中对医疗图像细分模型进行顺序训练. ITL提高了性能和概括性,同时防止了灾难性的遗忘.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 医疗图像细分任务可以从大型数据集中获益.
- 现有的多站点培训方法需要同时收集所有数据,这限制了实际部署.
- 需要对单个模型进行顺序训练,以提高性能和通用化.
研究的目的:
- 提出一种新的增量转移学习 (ITL) 框架,用于连续的多站点医疗图像细分.
- 为了使一个单一的模型在数据集中表现更好,并将其推广到新的领域.
- 在增量学习中解决灾难性遗忘问题.
主要方法:
- 开发了一个端到端的连续培训框架 (ITL).
- 使用了带有预训练重量和多个解码器头的站点无关编码器.
- 引入了网站级增量损失以改善概括.
- 利用嵌入特征的杆线性组合进行知识传输.
主要成果:
- 证明了ITL在缓解灾难性遗忘方面的有效性.
- 在五个具有挑战性的基准数据集上验证了方法.
- 与现有方法相比,实现了性能和通用性的改进.
结论:
- ITL为连续的多站点医疗图像细分提供了强大的解决方案.
- 该框架尽量减少对计算资源和专业知识的假设.
- ITL为该领域的未来进步提供了坚实的基础.
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