A Lightweight Neural Architecture Search Model for Medical Image Classification

arXiv:2405.03462v1 Announce Type: cross
Abstract: Accurate classification of medical images is essential for modern diagnostics. Deep learning advancements led clinicians to increasingly use sophisticated models to make faster and more accurate decisions, sometimes replacing human judgment. However, model development is costly and repetitive. Neural Architecture Search (NAS) provides solutions by automating the design of deep learning architectures. This paper presents ZO-DARTS+, a differentiable NAS algorithm that improves search efficiency through a novel method of generating sparse probabilities by bi-level optimization. …

abstract architecture arxiv classification clinicians cs.ai cs.cv cs.lg decisions deep learning development diagnostics faster however human image images judgment medical model development modern nas neural architecture search search solutions type