使用可解释的人工智能解密白血病诊断中的深度学习决策.
Shahd H Altalhi1, Salha M Alzahrani1
1Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了使用深度学习和可解释AI (XAI) 来准确诊断细胞图像的白血病的人工智能管道. 人工智能实现了高精度,识别了关键的细胞特征,以获得可靠的结果.
科学领域:
- 血液学 血液学 血液学
- 计算生物学 计算生物学
- 医疗成像医学成像
背景情况:
- 传统的白血病诊断依赖于专家对外围血液涂抹和骨髓评估的解释.
- 这些方法面临着由于生物和成像变化的挑战,需要先进的诊断工具.
- 现有的分子技术,如LDI-PCR,分子细胞遗传学和数组-CGH补充诊断,但很复杂.
研究的目的:
- 开发和验证人工智能管道,用于准确和可解释的白血病诊断.
- 整合卷积神经网络 (CNN) 和可解释AI (XAI) 进行透明的诊断逻辑.
- 建立一个全面的基准数据集,用于评估白血病分类中的AI模型.
主要方法:
- 统一的基准数据集由66,550张图像组成,涵盖各种白血病类型 (ALL,AML,CLL,CML) 和健康对照.
- 多个CNN的骨干 (DenseNet-121,MobileNetV2,等等) 的使用. 被微调并使用准确度和F1得分指标进行评估.
- 使用可解释的AI技术 (LIME,Grad-Cam) 为AI的诊断决策提供了透明的理由.
主要成果:
- 移动NetV2在五类白血病分类任务中实现了97.9%的准确性/F1.
- 丹森网-121也表现出高性能,97.66%的F1得分,显示出强大的以核为中心的解释.
- XAI成功地分析了局部关键细胞形态,将模型突出性与临床指标对齐.
结论:
- 拟议的AI管道,整合CNN和XAI,在白血病诊断中实现了最先进的准确性.
- 可解释的AI方法提供了关键的解释性,证实了具有临床相关性的诊断发现.
- 这种方法为血液恶性瘤的传统诊断工作流提供了更准确和透明的替代方案.
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