An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging

TL;DR

This paper introduces Retformer, an explainable transformer-based model that detects Alzheimer's disease from retinal images, providing interpretability and outperforming existing methods.

Contribution

The study presents a novel transformer architecture for AD detection using retinal imaging and incorporates explainability through feature importance visualization.

Findings

Retformer outperforms benchmark algorithms by up to 11% in various metrics.

Gradient-weighted Class Activation Mapping highlights key retinal regions for AD detection.

The model effectively learns complex patterns from multi-modal retinal datasets.

Abstract

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\.