Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912403606732800 |
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| author | Hashmi, Anam Dietlmeier, Julia Curran, Kathleen M. O'Connor, Noel E. |
| author_facet | Hashmi, Anam Dietlmeier, Julia Curran, Kathleen M. O'Connor, Noel E. |
| contents | Attention is a fundamental component of the human visual recognition system. The inclusion of attention in a convolutional neural network amplifies relevant visual features and suppresses the less important ones. Integrating attention mechanisms into convolutional neural networks enhances model performance and interpretability. Spatial and channel attention mechanisms have shown significant advantages across many downstream tasks in medical imaging. While existing attention modules have proven to be effective, their design often lacks a robust theoretical underpinning. In this study, we address this gap by proposing a non-linear attention architecture for cardiac MRI reconstruction and hypothesize that insights from ecological principles can guide the development of effective and efficient attention mechanisms. Specifically, we investigate a non-linear ecological difference equation that describes single-species population growth to devise a parameter-free attention module surpassing current state-of-the-art parameter-free methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_23872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction Hashmi, Anam Dietlmeier, Julia Curran, Kathleen M. O'Connor, Noel E. Image and Video Processing Computer Vision and Pattern Recognition Attention is a fundamental component of the human visual recognition system. The inclusion of attention in a convolutional neural network amplifies relevant visual features and suppresses the less important ones. Integrating attention mechanisms into convolutional neural networks enhances model performance and interpretability. Spatial and channel attention mechanisms have shown significant advantages across many downstream tasks in medical imaging. While existing attention modules have proven to be effective, their design often lacks a robust theoretical underpinning. In this study, we address this gap by proposing a non-linear attention architecture for cardiac MRI reconstruction and hypothesize that insights from ecological principles can guide the development of effective and efficient attention mechanisms. Specifically, we investigate a non-linear ecological difference equation that describes single-species population growth to devise a parameter-free attention module surpassing current state-of-the-art parameter-free methods. |
| title | Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.23872 |