通过自我监督和分布校准来提高少数发射罕见皮肤病的分类
Wen Fu1,2, Jie Chen1, Li Zhou1
1Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029 China.
Biomedical engineering letters
|July 1, 2024
概括
本研究引入了一种自我监督分发校准网络 (SS-DCN),以提高使用少数射击学习的罕见皮肤病分类. 该方法通过解决有限数据集中的数据稀缺性和偏差分布,提高了模型的概括性.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 皮肤病学 皮肤病学
背景情况:
- 罕见的皮肤疾病对深度学习分类提出了数据稀缺的挑战.
- 现有的少数射击学习方法在有限的数据上与细粒度的分类和偏差分布作斗争.
研究的目的:
- 开发一个自我监督分发校准网络 (SS-DCN) 以改善罕见皮肤疾病的分类.
- 为了解决数据稀缺性,细粒度分类问题,以及在少量学习场景中的偏差分布.
主要方法:
- 采用多任务学习框架,自主监督的任务用于学习歧视性表示的预训.
- 引入了增强分布校准 (EDC) 策略,使用基类统计数据校准新型类分布.
- 从校准分布生成额外的样本,以增强分类员的培训.
主要成果:
- 在ISIC2018,Derm7pt和SD198数据集上实现了显著的性能改进.
- 在罕见皮肤疾病分类中,与最先进的方法相比,表现出更高的概括性.
- 有效地缓解了数据稀缺性和偏差分布在少量学习中产生的问题.
结论:
- 拟议的SS-DCN有效地提高了罕见皮肤病分类的准确性和通用性.
- 自我监督和分配校准对于医疗成像中的强大的少数镜头学习至关重要.
- SS-DCN提供了一个有前途的解决方案,用于诊断罕见的皮肤病,但数据有限.
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