Paper: Oct 13,2024
eess.IV
ID:2410.09674
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Neuromorphic computing has emerged as a promising energy-efficient
alternative to traditional artificial intelligence, predominantly utilizing
spiking neural networks (SNNs) implemented on neuromorphic hardware.
Significant advancements have been made in SNN-based convolutional neural
networks (CNNs) and Transformer architectures. However, their applications in
the medical imaging domain remain underexplored. In this study, we introduce
EG-SpikeFormer, an SNN architecture designed for clinical tasks that integrates
eye-gaze data to guide the model's focus on diagnostically relevant regions in
medical images. This approach effectively addresses shortcut learning issues
commonly observed in conventional models, especially in scenarios with limited
clinical data and high demands for model reliability, generalizability, and
transparency. Our EG-SpikeFormer not only demonstrates superior energy
efficiency and performance in medical image classification tasks but also
enhances clinical relevance. By incorporating eye-gaze data, the model improves
interpretability and generalization, opening new directions for the application
of neuromorphic computing in healthcare.
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Paper Author: Yi Pan,Hanqi Jiang,Junhao Chen,Yiwei Li,Huaqin Zhao,Yifan Zhou,Peng Shu,Zihao Wu,Zhengliang Liu,Dajiang Zhu,Xiang Li,Yohannes Abate,Tianming Liu
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