用4D卷积模型同时捕捉脑部功能MRI的时空动态，提升阿尔茨海默病早期诊断精度。
Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
- 设计4D卷积网络，联合学习时间与空间特征
- 在静息态fMRI数据上表现优于传统3D模型
- 适合关注脑疾病早期诊断的研究者
以往研究在4D功能MRI数据上仅使用3D空间模型，可能导致特征提取不足。本文提出一种新型4D卷积神经网络，能够学习4D联合时空核，同时捕捉空间结构与时间动态。实验表明，该模型在功能MRI的时空特征建模上显著优于3D模型，提升了阿尔茨海默病的诊断性能，有助于实现更早检测和干预。未来可拓展至任务态fMRI及回归任务，深化对认知功能与疾病进展的理解。
原文摘要 · Abstract (English)
Previous works in the literature apply 3D spatial-only models on 4D functional MRI data leading to possible sub-par feature extraction to be used for downstream tasks like classification. In this work, we aim to develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels that not only learn spatial information but in addition also capture temporal dynamics. Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D models. The 4D CNN model improves Alzheimers disease diagnosis for rs-fMRI data, enabling earlier detection and better interventions. Future research could explore task-based fMRI applications and regression tasks, enhancing understanding of cognitive performance and disease progression.
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