2022/09/30 by Rakshith Sathish, Swanand Khare, Sathish, Rakshith +3 · 1 citation
Computer Science · Medicine · Neuroscience · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.2209.15287
openalex publication_date 2022/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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-Networkhttps://github.com/Rakshith2597/Quantised-Self-Attentive-Deep-Neural-Network.