Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks

• URL: http://arxiv.org/abs/2209.15287v1
• Date: Fri, 30 Sep 2022 07:51:29 GMT
• Title: Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks
• Abstract summary: We propose quantised stand-alone self-attention based models as an alternative to traditional CNNs. We experimentally validate the performance of our method on classification and segmentation tasks.
• Score: 7.751856268560217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.

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