Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks. (arXiv:2209.15287v1 [cs.CV])

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 …

analysis arxiv energy energy efficient image medical networks neural networks