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

Ke Chen, Yifeng Wang, Yufei Zhou, Haohan Wang
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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.
DS-ViT:用于阿尔茨海默氏症早期诊断中跨任务蒸馏的双流视觉转换器
在阿尔茨海默病诊断领域,分割和分类任务本质上是相互关联的。在这些任务的模型之间共享知识可以显著提高训练效率,尤其是在训练数据稀缺的情况下。然而,由于任务的不同性质和模型架构的不同,传统的知识发散技术往往难以弥合分割和分类之间的差距。为了应对这一挑战,我们提出了一种双流管道,以促进跨任务和跨架构的知识共享。我们的方法引入了一个双流嵌入模块,该模块统一了来自分割和分类模型的特征表示,使这些特征的维度整合能够指导分类模型。我们在用于阿尔茨海默病诊断的多个三维数据集上验证了我们的方法,结果表明分类性能显著提高,尤其是在小型数据集上。此外,我们还利用在患者脑组织萎缩之前拍摄的图像,扩展了用于早期诊断的时间注意力机制。这一进步有望使轻度和无症状阶段的诊断提前约六个月,为干预提供关键时间。
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