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Trans-ResNet: Integrating Transformers and CNNs for Alzheimer's disease classification
Li C(李超)1,2; Cui Y(崔玥)1,2; Luo N(罗娜)1,2; Liu Y(刘勇)1,2; Bourgeat Pierrick3; Fripp Jurgen3; Jiang TZ(蒋田仔)1,2
2022-03
会议名称2022 IEEE 19th International Symposium on Biomedical Imaging
会议日期2022-3-28
会议地点印度,加尔各答
出版者Institute of Electrical and Electronics Engineers
摘要

Convolutional neural networks (CNNs) have demonstrated excellent performance for brain disease classification from MRI data. However, CNNs lack the ability to capture global dependencies. The recently proposed architecture called Transformer uses attention mechanisms to match or even outperform CNNs on various vision tasks. Transformer’s performance is dependent on access to large training datasets, but sample sizes for most brain MRI datasets are relatively small. To overcome this limitation, we propose Trans-ResNet, a novel architecture which integrates the advantages of both CNNs and Transformers. In addition, we pre-trained our Trans-ResNet on a large-scale dataset on the task of brain age estimation for higher performance. Using three neuroimaging cohorts (UK Biobank, AIBL, ADNI), we demonstrated that our Trans-ResNet achieved higher classification accuracy on Alzheimer disease prediction compared to other state-of-the-art CNN-based methods.

关键词convolutional neural network deep learning structural MRI transfer learning Transformer
学科门类工学::计算机科学与技术(可授工学、理学学位)
DOI10.1109/ISBI52829.2022.9761549
收录类别EI
语种英语
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/48876
专题脑网络组研究
通讯作者Jiang TZ(蒋田仔)
作者单位1.中国科学院自动化研究所脑网络组
2.中国科学院大学
3.CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Australia
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Li C,Cui Y,Luo N,et al. Trans-ResNet: Integrating Transformers and CNNs for Alzheimer's disease classification[C]:Institute of Electrical and Electronics Engineers,2022.
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