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D-EDL:差异证据深度学习,用于强大的医疗外分发检测

Wei Fu¹, Yufei Chen¹, Yuqi Liu¹

¹School of Computer Science and Technology, Tongji University, Shanghai, 201804, China.

Medical image analysis

|December 4, 2025

概括

不同证据深度学习 (D-EDL) 通过减少对模两可的案件的过度处罚,提高了医疗AI中的分布外 (OOD) 检测. 这种方法通过更好地区分罕见疾病和常见疾病来提高诊断安全性.

科学领域:

计算机辅助诊断是一种计算机辅助的诊断.
机器学习在医疗保健中的应用
医疗图像分析医学图像分析

背景情况:

医学数据中的极端类失衡导致分布外 (OOD) 样本,冒着遗漏罕见疾病的风险.
证据深度学习 (EDL) 显示了OOD检测的希望,但与医疗图像的变化性作斗争,并过度惩罚模两可的分布样本.
临床诊断过程涉及确认和排除,这是当前的OOD方法经常忽视的原则.

研究的目的:

为计算机辅助诊断开发一个强大的分布外检测方法,解决现有的证据深度学习方法的局限性.
提高人工智能诊断系统的临床安全性和可靠性,特别是在罕见疾病流行情况的情况下.
加强在分布中的模两可的样本和分布之外的真实样本之间的区别.

主要方法:

提出差异证据深度学习 (D-EDL),用排除模块 (ROM) 替代Kullback-Leibler (KL) 差异,用于差异限制.
引入测试时间原始证据推理 (RI) 以减轻不确定性估计的不稳定性,并改进证据以提高稳定性.
开发平衡检测得分 (BDS) 通过平衡错误诊断和错误诊断来评估临床表现.

主要成果:

在ISIC2019,骨髓细胞形态学和EDDFS数据集上,D-EDL显著超过了最先进的OOD检测方法.
拟议的方法在识别OOD样本方面表现出卓越的稳定性和精度,特别是在具有挑战性的医学成像场景中.
D-EDL有效地减少了对模两可的分布样本的过度惩罚效应,同时保持了对真异常值的高灵敏度.

结论:

与现有的方法相比,D-EDL在计算机辅助诊断中为OOD检测提供了更具临床适用性和更强大的解决方案.
综合差异诊断原则和精细的证据估计提高了AI在识别罕见或未见的条件的可靠性.
开发的方法具有显著的潜力,可以通过预防在临床实践中由于OOD样本导致的错误诊断来提高患者安全.

关键词:

计算机辅助诊断是指计算机辅助的诊断.有证据的深度学习.在分销之外的检测检测

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