DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer's Early Diagnosis (2409.07584v1)

Abstract: In the field of Alzheimer's disease diagnosis, segmentation and classification tasks are inherently interconnected. Sharing knowledge between models for these tasks can significantly improve training efficiency, particularly when training data is scarce. However, traditional knowledge distillation techniques often struggle to bridge the gap between segmentation and classification due to the distinct nature of tasks and different model architectures. To address this challenge, we propose a dual-stream pipeline that facilitates cross-task and cross-architecture knowledge sharing. Our approach introduces a dual-stream embedding module that unifies feature representations from segmentation and classification models, enabling dimensional integration of these features to guide the classification model. We validated our method on multiple 3D datasets for Alzheimer's disease diagnosis, demonstrating significant improvements in classification performance, especially on small datasets. Furthermore, we extended our pipeline with a residual temporal attention mechanism for early diagnosis, utilizing images taken before the atrophy of patients' brain mass. This advancement shows promise in enabling diagnosis approximately six months earlier in mild and asymptomatic stages, offering critical time for intervention.

• The paper introduces a dual-stream Vision Transformer that fuses MRI segmentation and classification to enhance early Alzheimer’s diagnosis, achieving a 7% accuracy boost in small-dataset scenarios.
• The methodology employs dual-stream embedding and a Residual Temporal Attention Block to integrate 3D MRI features and capture temporal dynamics for predictive diagnostics.
• Experimental results across multiple datasets confirm that DS-ViT reduces convergence time and improves diagnostic accuracy, underscoring its potential for timely intervention in Alzheimer’s care.

DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer’s Early Diagnosis

The paper "DS-ViT: Dual-Stream Vision Transformer for Cross-Task Distillation in Alzheimer’s Early Diagnosis" by Ke Chen et al. proposes a novel approach to enhance the training efficiency and diagnostic accuracy of Alzheimer’s Disease (AD) models by integrating segmentation and classification tasks. This work introduces a Dual-Stream Vision Transformer (DS-ViT) that leverages cross-task knowledge sharing between segmentation and classification models, which is particularly beneficial when dealing with small datasets.

Introduction

Alzheimer's Disease (AD) is a complex neurodegenerative disorder that demands early diagnosis for effective intervention. Traditional machine learning models treat segmentation and classification as separate tasks, which can lead to inefficiencies, particularly given the overlapping nature of these tasks in the medical domain. The authors argue for a more integrated approach that utilizes segmentation results to guide classification, especially leveraging MRI data to identify structural changes indicative of AD. Existing segmentation models like FastSurfer and classification models like ADAPT are leveraged, representing a significant step towards addressing this integration challenge.

Methodology

The DS-ViT pipeline is designed to achieve effective cross-task knowledge distillation, integrating detailed brain segmentation information from FastSurfer with the classification capabilities of ADAPT. It incorporates a dual-stream embedding module for processing pixel-level MRI data and token-like embeddings from segmentation maps. This unified representation guides a Vision Transformer (ViT) based diagnostic model.

1. Dual-Stream Embedding:
   • Stream 1: Processes the original MRI data, embedding pixel-level information akin to typical ViT approaches.
   • Stream 2: Processes segmentation maps, treating brain region labels as tokens to overcome the challenge of leveraging categorical segmentation data in a continuous MRI representation.
2. 3D Feature Integration:
   • The features from both streams are integrated using a 3D Bottleneck MLP structure, concatenating across three orthogonal planes. This integrated feature matrix serves as the input for the ADAPT classification model.
3. Residual Temporal Attention Block (RTAB):
   • For tasks requiring early diagnosis, the model includes a RTAB that analyzes temporal dynamics across sequential MRI scans. This attention mechanism captures residual changes between time points, enabling the prediction of future disease risk.

Experiments

The authors validated their approach using multiple MRI datasets (ADNI, MIRIAD, AIBL, OASIS). The DS-ViT model was assessed under both data-sufficient and data-scarce scenarios. The experimental results highlighted several key findings:

1. Performance Improvement:
   • DS-ViT consistently outperformed baseline models across datasets, demonstrating significant accuracy gains (e.g., a 7% improvement over ADAPT in small-dataset scenarios).
   • DS-ViT exhibited a notable reduction in convergence time, typically within 15 epochs compared to ADAPT's 30-60 epochs.
2. Ablation Studies:
   • Eliminating either the MRI or segmentation stream, or the dual-stream embedding, resulted in performance degradation, underscoring the importance of each component.
   • Traditional knowledge distillation methods (e.g., hint layer distillation) proved less effective compared to the proposed dual-stream integration.
3. Early Diagnosis:
   • DS-ViT+RTAB achieved an overall classification accuracy of 70.4%, with high-confidence samples reaching 86% accuracy. This suggests potential for predictive diagnostics up to six months in advance, allowing for earlier therapeutic interventions.

Implications and Future Directions

The implications of this work are multifaceted. Practically, the integration of segmentation information into AD diagnostic models can lead to more accurate and timely diagnoses, crucial for early-stage intervention. Theoretically, the cross-task distillation demonstrated by DS-ViT opens avenues for similar integrations in other medical and non-medical domains.

Future developmental directions could encompass extending temporal sequences for early diagnosis, refining attention mechanisms for low-confidence cases, and generalizing the pipeline to other neurodegenerative disorders. This paper contributes substantially to the progression of machine learning techniques applied to medical diagnostics, particularly in contexts where early detection can significantly influence patient outcomes.

In summary, the DS-ViT framework represents a promising advance in the field of AD diagnosis, providing a robust methodology for integrating segmentation and classification knowledge. This dual-stream approach effectively enhances both training efficiency and diagnostic performance, especially in scenarios with constrained data availability.