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Updated: Jan 26, 2026

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在低资源条件下使用转移学习和对比学习加上XAI来检测状细胞疾病
Jay Patel1, H Muralikrishna2, Krishnaraj Chadaga1
1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Scientific reports
|January 24, 2026
概括
转移学习和对比学习显著改善了用于状细胞疾病 (SCD) 检测的深度学习模型,即使数据有限. 这些先进的方法提高了临床应用中的准确性和可靠性.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 遗传学和基因组学 在
背景情况:
- 状细胞疾病 (SCD) 是一种普遍存在的遗传性血液疾病,影响全球数百万人.
- 准确和早期发现SCD对于改善患者的预后和结果至关重要.
- 目前用于自动SCD检测的深度学习 (DL) 模型需要广泛的训练数据集,这些数据集通常是不可用的.
研究的目的:
- 为了应对基于深度学习的SCD检测有限的培训数据的挑战.
- 为了提高自动化SCD检测模型的效率和准确性.
- 整合可解释的人工智能 (XAI) 以实现透明和可信的临床决策支持.
主要方法:
- 通过微调预训练模型 (ResNet-50,DenseNet-121,EfficientNet-B0) 用于SCD检测,利用转移学习.
- 整合了具有三倍损失的对比学习,以进一步提高模型性能.
- 使用焦点损失来管理数据集中的类不平衡.
- 综合可解释的人工智能 (XAI) 技术用于模型可解释性.
主要成果:
- 使用转移学习和三重损失的模型表现出优异的性能,与仅使用二进制交叉或焦点损失进行训练的模型相比.
- 提出的方法有效地解决了小型培训数据集带来的局限性.
- XAI集成为模型预测提供了洞察力,增强了临床信任.
结论:
- 转移学习与对比学习相结合,为使用有限数据检测SCD提供了强大的解决方案.
- 开发的模型显示了准确,高效和透明的自动化SCD诊断的前景.
- 整合XAI对于AI驱动的诊断工具在医疗保健中的临床采用至关重要.
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