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.

翻译：医学图像的精确分类对现代诊断至关重要。深度学习的进步促使临床医生越来越多地使用复杂模型来做出更快、更准确的决策，有时甚至取代了人工判断。然而，模型开发成本高昂且重复性强。神经架构搜索（NAS）通过自动化设计深度学习架构提供了解决方案。本文提出ZO-DARTS+，一种可微分的NAS算法，通过双层优化生成稀疏概率的新颖方法提升了搜索效率。在五个公开医学数据集上的实验表明，ZO-DARTS+在匹配现有最优解决方案准确率的同时，将搜索时间缩短了高达三倍。