Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs

arXiv:2403.05340v1 Announce Type: cross
Abstract: When deploying neural networks in real-life situations, the size and computational effort are often the limiting factors. This is especially true in environments where big, expensive hardware is not affordable, like in embedded medical devices, where budgets are often tight. State-of-the-art proposed multiple different lightweight solutions for such use cases, mostly by changing the base model architecture, not taking the input and output resolution into consideration. In this paper, we propose our architecture that takes …

abstract art arxiv big computational cs.cv cs.lg deployment devices eess.iv embedded environments hardware inputs life low medical medical devices medicine multiple networks neural networks segmentation semantic state through true type