Vision Mamba: Cutting-Edge Classification of Alzheimer's Disease with 3D MRI Scans

TL;DR

This paper introduces Vision Mamba, a novel model based on State Space Models, that efficiently classifies 3D MRI scans for early Alzheimer's detection, outperforming traditional CNN and Transformer approaches.

Contribution

The paper presents Vision Mamba, a new model leveraging dynamic state representations and selective scanning for improved accuracy and efficiency in 3D MRI classification.

Findings

Vision Mamba achieves higher accuracy than CNNs and Transformers.

The model demonstrates improved computational efficiency on 3D MRI data.

Experimental results validate its effectiveness for early Alzheimer's detection.

Abstract

Classifying 3D MRI images for early detection of Alzheimer's disease is a critical task in medical imaging. Traditional approaches using Convolutional Neural Networks (CNNs) and Transformers face significant challenges in this domain. CNNs, while effective in capturing local spatial features, struggle with long-range dependencies and often require extensive computational resources for high-resolution 3D data. Transformers, on the other hand, excel in capturing global context but suffer from quadratic complexity in inference time and require substantial memory, making them less efficient for large-scale 3D MRI data. To address these limitations, we propose the use of Vision Mamba, an advanced model based on State Space Models (SSMs), for the classification of 3D MRI images to detect Alzheimer's disease. Vision Mamba leverages dynamic state representations and the selective scan algorithm, allowing it to efficiently capture and retain important spatial information across 3D volumes. By dynamically adjusting state transitions based on input features, Vision Mamba can selectively retain relevant information, leading to more accurate and computationally efficient processing of 3D MRI data. Our approach combines the parallelizable nature of convolutional operations during training with the efficient, recurrent processing of states during inference. This architecture not only improves computational efficiency but also enhances the model's ability to handle long-range dependencies within 3D medical images. Experimental results demonstrate that Vision Mamba outperforms traditional CNN and Transformer models accuracy, making it a promising tool for the early detection of Alzheimer's disease using 3D MRI data.