走向人工智能辅助状细胞查:使用公共血液污染图像对CNN,变压器和混合架构进行受控比较
Linah Tasji1,2, Hanan S Alghamdi1, Abdullah S Almalaise Al-Ghamdi1,3
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
卷积神经网络 (CNN) 和混合模型在状细胞疾病 (SCD) 查中,使用血涂片图像,特别是有限的数据,优于纯变压器. 这些AI方法为SCD诊断提供了可靠的决策支持.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
- 计算病理学计算病理学
背景情况:
- 状细胞疾病 (SCD) 给健康带来了重大挑战,特别是在先进诊断技术稀缺的地方.
- 传统的SCD诊断方法是劳动密集型,缓慢,容易出现人为错误.
- 人工智能 (AI) 提供自动化血液涂抹分析作为可扩展的诊断替代方案.
研究的目的:
- 从外围血液涂抹图像进行自动状细胞疾病分类,对各种AI架构进行基准测试.
- 通过严格的,泄漏安全的数据分割和重复评估来评估模型的稳定性.
- 在资源有限的环境中确定SCD查的最佳AI策略.
主要方法:
- 使用公共血液涂抹数据集对卷积神经网络 (CNN),视觉转换器 (ViT) 和混合模型进行比较分析.
- 在相同的,泄漏安全条件下微调11个ImageNet预训练模型,并进行基于组的分割.
- 通过准确性,宏观平均F1分数,统计测试,错误分析和可解释AI (XAI) 的性能评估.
主要成果:
- 与仅使用变压器的模型相比,基于CNN和混合架构显示出优越的稳定性和性能,特别是在数据限制下.
- MaxViT-Tiny和DenseNet121取得了最高的性能;纯粹的ViT在有限的数据下效率较低.
- XAI表示,CNN专注于本地形态,而混合体则集成本地和上下文信息;在模两可的情况下,错误的阳性结果很常见.
结论:
- 卷积诱导偏差对于从血液涂抹中进行可靠的SCD分类至关重要,特别是在有限的数据的情况下.
- CNN和混合CNN-变压器模型为SCD查提供可解释和可靠的性能.
- 这些人工智能模型显示出在SCD查研究环境中作为决策支持工具的潜力.
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