Abstract / Summary
Alzheimer’s disease (AD) classification utilizing 3D Convolutional Neural Networks (CNNs) on brain MRI poses significant challenges, including high computational overhead, the scarcity of large-scale training data, and the inherent difficulty in capturing subtle pathological features. Existing 3D-to-2D compression methods simplify the data but suffer from critical information loss. We propose Robust Rank Pooling (RRP), a novel compression technique that employs a sliding window strategy to transform a 3D MRI volume into multiple 2D dynamic images. This approach effectively mitigates information loss by preserving robust structural features across different brain sections. We utilize these generated images to train an efficient, pre-trained 2D CNN architecture, which incorporates a model ensemble method to further enhance performance. The proposed method is validated on two T2-FLAIR MRI benchmarks, ADNI and BICWALZS. Experimental results demonstrate that our approach significantly outperforms existing methods. Compared to the previous method, our model achieves improvements of approximately 2.3% in accuracy and 4.4% in Area Under the Curve (AUC). Relative to a standard 3D network, our method improves accuracy by 13.2% while demonstrating a 4.1 \(\times \) and 2.1 \(\times \) speedups in model inference and total processing time, respectively. Furthermore, qualitative analysis confirms that our model focuses on clinically relevant biomarkers, consequently offering a computationally efficient and effective solution for Alzheimer’s disease classification.