利用可解释的3D混合紧凑卷积Transformer增强基于MRI的阿尔茨海默病分类
Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers
- Institute of Advancing Intelligence, TCG CREST（TCG CREST 智能推进研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出3D混合紧凑卷积Transformer（3D HCCT），融合CNN与ViT以同时捕获3D MRI局部特征和长程关系，并在ADNI上取得优于现有方法的AD分类精度与可解释性。
AI中文摘要：
阿尔茨海默病（AD）以进行性认知衰退和记忆丧失为特征，构成了严峻的全球健康挑战，凸显了早期精准诊断对于及时干预和改善患者预后的至关重要性。虽然MRI扫描能够提供关于大脑结构的有价值信息，但传统分析方法往往难以辨别对AD识别至关重要的复杂3D模式。针对这一挑战，我们提出了一种替代性的端到端深度学习模型——3D Hybrid Compact Convolutional Transformers 3D（HCCT）。通过协同结合卷积神经网络（CNNs）和视觉Transformer（ViTs），3D HCCT能够熟练地同时捕获3D MRI扫描中的局部特征和长程关系。在著名的AD基准数据集ADNI上的广泛评估表明，3D HCCT具有优越的性能，在分类准确率上超越了最先进的基于CNN和Transformer的方法。其强大的泛化能力和可解释性标志着基于3D MRI扫描的AD分类取得了重大进展，有望实现更准确、更可靠的诊断，从而改善患者护理并获得更优的临床结果。
英文摘要：
Alzheimer's disease (AD), characterized by progressive cognitive decline and memory loss, presents a formidable global health challenge, underscoring the critical importance of early and precise diagnosis for timely interventions and enhanced patient outcomes. While MRI scans provide valuable insights into brain structures, traditional analysis methods often struggle to discern intricate 3D patterns crucial for AD identification. Addressing this challenge, we introduce an alternative end-to-end deep learning model, the 3D Hybrid Compact Convolutional Transformers 3D (HCCT). By synergistically combining convolutional neural networks (CNNs) and vision transformers (ViTs), the 3D HCCT adeptly captures both local features and long-range relationships within 3D MRI scans. Extensive evaluations on prominent AD benchmark dataset, ADNI, demonstrate the 3D HCCT's superior performance, surpassing state of the art CNN and transformer-based methods in classification accuracy. Its robust generalization capability and interpretability marks a significant stride in AD classification from 3D MRI scans, promising more accurate and reliable diagnoses for improved patient care and superior clinical outcomes.
