一种基于视网膜成像的阿尔茨海默病检测可解释Transformer模型
An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging
- Nanotechnology, Biotechnology, Information Technology and Cognitive Science Laboratory, University of Tehran（特拉布宗大学纳米技术、生物技术、信息技术和认知科学实验室）
- Department of Mechatronics, School of Intelligent Systems, College of Interdisciplinary Science and Technology, University of Tehran（特拉布宗大学机电系，智能系统学院，跨学科科学与技术学院）
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England（新英格兰大学科学与技术学院，科学、农业、商业与法律学院）
- Graduate School of Health, University of Technology Sydney（悉尼技术大学健康研究生院）
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle（新castle大学医学与公共卫生学院，健康、医学与福祉学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于Transformer的可解释模型Retformer，利用多模态视网膜图像检测阿尔茨海默病，并通过Grad-CAM可视化关键特征，性能超越基准算法最高达11%。
AI中文摘要：
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。在缺乏有效治疗方案的情况下，早期诊断对于启动管理策略以延缓疾病发作并减缓其进展至关重要。在本研究中，我们提出Retformer，一种基于Transformer的新型架构，利用Transformer和可解释人工智能的能力，通过视网膜成像模态检测AD。Retformer模型在来自AD患者和年龄匹配的健康对照的不同模态视网膜图像数据集上进行训练，使其能够学习图像特征与疾病诊断之间的复杂模式和关系。为了深入了解我们模型的决策过程，我们采用Gradient-weighted Class Activation Mapping算法可视化特征重要性图，突出显示对视网膜图像分类结果贡献最大的区域。这些发现与现有利用视网膜生物标志物检测AD的临床研究进行比较，使我们能够识别每种成像模态中用于AD检测的最重要特征。Retformer模型在不同性能指标上以最高达11%的优势超越多种基准算法。
英文摘要：
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\.
