基于信心和不确定性的推断时间校正,以提高医学图像分类中的深度学习模型性能和可解释性
Joel Jeffrey1, Ashwin RajKumar1, Sudhanshu Pandey1
1Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, 560012, India.
概括
一个新的算法,基于信心和的不确定性值算法 (CEbUTAl),通过解决类不平衡和提高可解释性而改善人工智能 (AI) 医疗图像分析.
科学领域:
- 医学图像分析
- 人工智能
- 机器学习
背景情况:
- 训练数据中的阶级不平衡和有限的解释性是医疗图像分析中人工智能的重大挑战.
- 现有方法通常需要在模型性能和可解释性之间进行权衡.
研究的目的:
- 引入新的后处理算法CEbUTAl,以提高医疗成像中的AI模型的性能和可解释性.
- 解决阶级不平衡问题,提高临床环境中人工智能模型的可靠性.
主要方法:
- 开发了基于信心和的不确定性值算法 (CEbUTAl) 作为一种模型不可知,任务不可知后处理技术.
- 在各种深度学习架构和丢失函数中,CEbUTAl应用于五个医学成像任务,包括内出血检测和乳腺癌检测.
主要成果:
- CEbUTAl提高了分类准确度约5%,并在多个任务和模型中提高了灵敏度.
- 在解决阶级不平衡和量化不确定性方面超越了最先进的方法.
- 证明增强可解释性不需要在人工智能模型性能上做出妥协.
结论:
- CEbUTAl提供了一种可通用的方法来缓解类不平衡的偏见,并改善医学成像中的AI解释性.
- 该算法提高了人工智能模型在临床实践中的实用性和可靠性.
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