GFE-Mamba：基于Mamba的AD多模态进展评估，通过从MCI生成式特征提取实现
GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI
AI总结：
针对阿尔茨海默病多模态数据收集困难的问题，提出基于生成式特征提取器的多模态分类器GFE-Mamba，结合Mamba模块和像素级双向交叉注意力，有效预测MCI到AD的进展并超越现有方法。
摘要：
阿尔茨海默病（AD）是一种进行性、不可逆的神经退行性疾病，通常源于轻度认知障碍（MCI）。这种病情进展会导致严重的记忆丧失，并严重影响患者的生活质量。临床试验持续表明，对MCI个体进行早期且有针对性的干预，可能会减缓甚至预防AD的进展。研究表明，准确的医学分类需要多样化的多模态数据，包括详细的评估量表以及磁共振成像（MRI）和正电子发射断层扫描（PET）等神经影像技术。然而，同时收集上述三种模态用于训练带来了巨大的挑战。为了解决这些困难，我们提出了GFE-Mamba，一种基于生成式特征提取器的多模态分类器。该提取器提供的中间特征可以弥补PET的不足，并在分类器中实现深度的多模态融合。Mamba模块作为分类器的骨干网络，使其能够高效地从长序列尺度信息中提取信息。像素级双向交叉注意力补充了来自MRI和PET的像素级信息。我们提供了开发这一跨时间进展预测数据集以及预训练提取器权重的理由。我们的实验结果表明，GFE-Mamba模型能有效预测从MCI到AD的进展，并超越了该领域内的几种领先方法。源代码可在https://github.com/Tinysqua/GFE-Mamba获取。
英文摘要：
Alzheimer's Disease (AD) is a progressive, irreversible neurodegenerative disorder that often originates from Mild Cognitive Impairment (MCI). This progression results in significant memory loss and severely affects patients' quality of life. Clinical trials have consistently shown that early and targeted interventions for individuals with MCI may slow or even prevent the advancement of AD. Research indicates that accurate medical classification requires diverse multimodal data, including detailed assessment scales and neuroimaging techniques like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). However, simultaneously collecting the aforementioned three modalities for training presents substantial challenges. To tackle these difficulties, we propose GFE-Mamba, a multimodal classifier founded on Generative Feature Extractor. The intermediate features provided by this Extractor can compensate for the shortcomings of PET and achieve profound multimodal fusion in the classifier. The Mamba block, as the backbone of the classifier, enables it to efficiently extract information from long-sequence scale information. Pixel-level Bi-cross Attention supplements pixel-level information from MRI and PET. We provide our rationale for developing this cross-temporal progression prediction dataset and the pre-trained Extractor weights. Our experimental findings reveal that the GFE-Mamba model effectively predicts the progression from MCI to AD and surpasses several leading methods in the field. Our source code is available at https://github.com/Tinysqua/GFE-Mamba.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
