LSTM-SENet: category decoding model with an attention mechanism

Penghui Ding; Chi Zhang; Linyuan Wang; Guoen Hu; Bin Yan; Li Tong

doi:10.1117/12.2644595

12 October 2022 LSTM-SENet: category decoding model with an attention mechanism

Penghui Ding, Chi Zhang, Linyuan Wang, Guoen Hu, Bin Yan, Li Tong

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123422M (2022) https://doi.org/10.1117/12.2644595
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

With the development of deep neural networks (DNN), building visual decoding models based on functional magnetic resonance imaging (fMRI) to simulate the visual system of the human brain and studying visual mechanisms have become a research hotspot. Although existing visual decoding models built using DNNs have achieved a certain accuracy, most models ignore the differences between different voxels. Among them, the BRNN-based category decoding model uses the bidirectional long short term memory (LSTM) network to simulate the visual bidirectional information flow, which improves the decoding accuracy, but it uses the voxels of each brain area as an overall input model. Therefore, we embed the channel attention module, the Squeeze-and-Excitation Networks (SENet), into the LSTM network to construct an LSTM-SENet vision that introduces an attention mechanism The decoding model allows the model to learn by itself and assign different weights to each voxel, focusing on important voxels, thereby improving the classification accuracy of natural images. The experimental results show that our method improves the accuracy of (three-level) category decoding than other methods, and the results further verify the effectiveness of building a visual decoding model based on the visual mechanism.

Citation Download Citation

Penghui Ding, Chi Zhang, Linyuan Wang, Guoen Hu, Bin Yan, and Li Tong "LSTM-SENet: category decoding model with an attention mechanism", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123422M (12 October 2022); https://doi.org/10.1117/12.2644595

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KEYWORDS

Visualization

Brain

Information visualization

Functional magnetic resonance imaging

Performance modeling

Neural networks

Visual cortex

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