用眼底影像和可解释Transformer模型，提前识别阿尔茨海默病。
An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging
- 基于Transformer架构，融合多模态眼底图像特征
- 在多个指标上优于基准模型，最高提升11%
- 通过可视化定位关键病变区域，支持临床决策
阿尔茨海默病（AD）是一种影响全球数百万人的神经退行性疾病。由于缺乏有效治疗手段，早期诊断对延缓发病和减缓进展至关重要。本文提出Retformer，一种基于Transformer的新型架构，用于利用眼底成像检测AD，结合变换器的强大表征能力与可解释人工智能技术。Retformer在来自阿尔茨海默病患者及年龄匹配健康对照者的多模态眼底图像数据集上进行训练，以学习图像特征与疾病诊断间的复杂模式与关联。为揭示模型决策过程，我们采用梯度加权类激活映射（Grad-CAM）算法生成特征重要性热图，突出对分类结果贡献最大的眼底图像区域。这些发现与现有临床研究中使用眼底生物标志物检测AD的结果对比，有助于识别各成像模态中最重要的判别特征。Retformer在多个性能指标上超越多种基准算法，提升幅度最高达11%。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide. In the absence of effective treatment options, early diagnosis is crucial for initiating management strategies to delay disease onset and slow down its progression. In this study, we propose Retformer, a novel transformer-based architecture for detecting AD using retinal imaging modalities, leveraging the power of transformers and explainable artificial intelligence. The Retformer model is trained on datasets of different modalities of retinal images from patients with AD and age-matched healthy controls, enabling it to learn complex patterns and relationships between image features and disease diagnosis. To provide insights into the decision-making process of our model, we employ the Gradient-weighted Class Activation Mapping algorithm to visualize the feature importance maps, highlighting the regions of the retinal images that contribute most significantly to the classification outcome. These findings are compared to existing clinical studies on detecting AD using retinal biomarkers, allowing us to identify the most important features for AD detection in each imaging modality. The Retformer model outperforms a variety of benchmark algorithms across different performance metrics by margins of up to 11\.
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