A Lightweight Neural Architecture Search Model for Medical Image Classification
This addresses efficiency in model development for clinicians using deep learning in medical diagnostics, though it appears incremental as it builds on existing NAS methods.
The paper tackles the problem of costly and repetitive model development for medical image classification by introducing ZO-DARTS+, a differentiable Neural Architecture Search algorithm that improves search efficiency. Experiments on five public medical datasets show it matches state-of-the-art accuracy while reducing search times by up to three times.
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.