双重胸部X射线肺炎分类中的幻觉隐患
Sivaramakrishnan Rajaraman1, Zhaohui Liang1, Niccolo Marini1
1Division of Intramural Research, National Library of Medicine, National Institutes of Health Bethesda, MD, USA.
概括
深度学习 (DL) 分类器经常幻觉,在胸部X射线上自信地错误分类肺炎亚型. 这些模型依赖于一般指标,而不是特定的病原体特征,限制了临床使用.
科学领域:
- 医学成像分析
- 医疗保健中的人工智能
- 放射学研究
背景情况:
- 深度学习 (DL) 模型可以表现出"幻觉",做出自信但不正确的预测.
- 由于放射性特征重叠,使用胸部X射线 (CXR) 区分肺炎亚型是具有挑战性的.
- 在识别病原体特异性肺炎时,二元分类器的可靠性需要彻底调查.
研究的目的:
- 评估二元分类器是否在CXR上学习肺炎亚型的疾病特征.
- 评估不确定性测量对病原体特异性肺炎的有用性.
- 调查DL分类器中的"幻觉"现象,
主要方法:
- 训练了两个二进制分类器来区分细菌性肺炎和病毒性肺炎与正常的CXR.
- 开发了第三个分类器,以区分细菌和病毒性肺炎的呈现.
- 在儿科CXR数据集上计算了马修斯相关系数和预测.
主要成果:
- 经过训练以区分正常和细菌性或病毒性肺炎的分类人员经常错误地分类未见的肺炎亚型.
- 模型对错误的预测表现出很高的信心,表明无法学习病原体的特定特征.
- 不确定性措施没有有效地防止重叠的肺炎呈现的错误分类.
结论:
- 二元DL分类器倾向于通过关注一般肺炎指标而不是特定的病因特征来产生幻觉.
- 目前的方法挑战了这些DL模型对于精确的肺炎亚型分类的临床实用性.
- 需要进一步研究以开发可靠地从CXR识别病原体特异性肺炎的DL模型.
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