  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. Experiments on five
public medical datasets show that ZO-DARTS+ matches the accuracy of
state-of-the-art solutions while reducing search times by up to three times.
