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稀少的数据,丰富的结果:通过课堂条件的图像翻译,通过少量拍摄的半监督学习
Guido Manni1, Clemente Lauretti2, Loredana Zollo2
1Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy; Unit of Advanced Robotics and Human-Centered Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
概括
这项研究引入了一种基于GAN的医学成像半监督学习框架,显著改善了使用最小标记数据进行分类. 该方法在低数据场景中表现出色,为昂贵的注释提供了实用解决方案.
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:
- 医学成像中的深度学习受到有限的标记数据的阻碍.
- 现有的方法在低数据模式下扎,增加了注释成本.
- 开发具有稀缺注释的有效模型是一个关键的挑战.
研究的目的:
- 为医疗成像引入基于GAN的新型半监督学习框架.
- 为了应对缺乏标记训练数据的挑战,在低数据制度中.
- 以最少的标记样本来实现强大的分类性能.
主要方法:
- 一个三阶段的培训框架,整合了一个生成器,区分器和分类器.
- 在有限的标记数据上交替监督培训,通过图像到图像翻译进行无监督学习.
- 基于集团的伪标签,使用指数移动平均值进行信心权重和时间一致性.
主要成果:
- 在11个MedMNIST数据集中,在6种基于GAN的半监督方法上进行了统计学上显著的改进.
- 在极端的5射击设置中表现出色,在最小的标记数据中证明了有效性.
- 在所有评估的设置中始终保持优势 (每班5,10,20和50次射击).
结论:
- 拟议的框架为医疗成像应用提供了一个实用的解决方案,其注释成本过高.
- 即使使用极其有限的标记数据,也能够实现强大的分类性能.
- 在数据稀缺的医学成像场景中展示了基于GAN的半监督学习的潜力.
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