Convolutional Neural Networks have played a significant role in various medical imaging tasks like classification and segmentation. They provide state-of-the-art performance compared to classical image processing algorithms. However, the major downside of these methods is the high computational complexity, reliance on high-performance hardware like GPUs and the inherent black-box nature of the model. In this paper, we propose quantised stand-alone self-attention based models as an alternative to traditional CNNs. In the proposed class of networks, convolutional layers are replaced with stand-alone self-attention layers, and the network parameters are quantised after training. We experimentally validate the performance of our method on classification and segmentation tasks. We observe a 50−80% reduction in model size, 60−80% lesser number of parameters, 40−85% fewer FLOPs and 65−80% more energy efficiency during inference on CPUs. The code will be available at \href {https://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neural-Network}{https://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neural-Network}.
@article{arxiv.2209.15287,
title = {Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks},
author = {Rakshith Sathish and Swanand Khare and Debdoot Sheet},
journal= {arXiv preprint arXiv:2209.15287},
year = {2022}
}