2025/05/29 by Anam Hashmi, Julia Dietlmeier, Hashmi, Anam +5
Engineering · Medicine · #Advanced MRI Techniques and Applications #Advanced X-ray and CT Imaging #Artificial neural network #Attention network #Channel (broadcasting) #Component (thermodynamics) #Computer Vision and Pattern Recognition (cs.CV) #Convolutional neural network #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Image and Video Processing (eess.IV) #Integrated Circuits and Semiconductor Failure Analysis #Population #Visual attention #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2505.23872
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.