Details

Autor(en) / Beteiligte

• Qiu, Zifeng
• Yang, Peng
• Wang, Tianfu
• Lei, Baiying

Titel

Hybrid Network Based on Cross-Modal Feature Fusion for Diagnosis of Alzheimer’s Disease

Ist Teil von

• Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging, p.87-99

Ort / Verlag

Cham: Springer Nature Switzerland

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Early diagnosis of Alzheimer’s disease (AD) (e.g., mild cognitive impairment, MCI), timely intervention, and treatment will effectively delay the further development of AD. Structural Magnetic Resonance Imaging (sMRI) and Positron Emission Computed Tomography (PET) play an essential role in diagnosing AD and MCI as they show signs of morphological changes in brain atrophy. However, it is difficult to learn more comprehensive information to diagnose AD and MCI thoroughly by single-modality brain imaging data, and it is challenging to locate the lesion area accurately. For diagnosing AD and MCI, convolutional neural networks (CNNs) have shown quite promising performance. However, the ability of CNNs to model global information is limited due to the properties of CNN sliding windows. In contrast, Transformer lacks modeling of local invariance, but it utilizes a self-attention mechanism to model long-term dependencies. Therefore, a novel cross-modal feature fusion based CNN-Transformer framework for AD and MCI diagnosis has been proposed. Specifically, we firstly exploit a large kernel attention (LKA) module to learn the attention map. Then, there will be two branches, CNN and Transformer, which are utilized to extract higher-level local and global features further. Furthermore, we utilize a modality feature fusion block to fuse the features of the two modalities. The extensive experimental results on the ADNI dataset show that our model outperforms the state-of-the-art methods.