基于自动编码器和视觉变压器的可解释性分析,对第二和第三牙的自动分期的性能差异进行了分析
Barkin Buyukcakir1, Jannick De Tobel2, Patrick Thevissen3
1Department of Electrical Engineering (ESAT) - Processing Speech and Images (PSI), KU Leuven, Leuven, 3000, Belgium. barkin.buyukcakir@kuleuven.be.
Scientific reports
|November 26, 2025
概括
这项研究介绍了一个深度学习框架,它结合了自动编码器和视觉变压器,用于透明的牙年龄估计. 该方法提高了准确性,并识别了数据的局限性,改善了法医决策.
科学领域:
- 法医科学 法医科学 法医科学
- 人工智能的人工智能
- 生物医学成像技术 生物医学成像技术
背景情况:
- 在法医应用中的深度学习模型,如牙科年龄估计,通常受到其"黑子"性质的限制,阻碍了实际采用.
- 在自动化牙科年龄估计中存在性能差异,特别是在不同类型的牙之间.
研究的目的:
- 引入一个新的框架,以提高法医牙科年龄估计的深度学习的性能和透明度.
- 解决"黑子"问题,并提供多方面的诊断见解,以改善决策.
主要方法:
- 开发了一个将卷积自编码器 (AE) 与视觉变压器 (ViT) 结合在一起的框架.
- 该框架是使用一个案例研究评估下第二 (牙37) 和第三 (牙38) 牙分期表现差异的框架.
- 来自AE的潜在空间指标和图像重建被分析以了解模型不确定性.
主要成果:
- 拟议的AE-ViT框架提高了对基线ViT的分类精度:牙37的分类精度从0.712提高到0.815,牙38的分类精度从0.462提高到0.543.
- 分析表明,剩余的性能差距是以数据为中心的,牙38数据集中的高类内形态变异性是关键限制.
- 该研究表明,仅依赖于可解释性方法 (如注意力图) 不足以识别潜在的数据问题.
结论:
- 开发的框架通过提高准确性和提供模型不确定性的证据,为法医年龄估计提供了更强大的工具.
- 这种方法通过提供透明度和识别以数据为中心的局限性来支持专家的决策.
- 多方面的解释性至关重要,超越像注意力图这样的单一方法来发现与数据相关的挑战.
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