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. 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.
Abstract (translated)
准确地分类医学图像对于现代诊断至关重要。深度学习的发展使得临床医生越来越依赖复杂的模型来做出更快、更准确的决策，有时甚至取代了人类的判断。然而，模型开发成本高且重复。神经架构搜索（NAS）通过自动设计深度学习架构提供了解决方案。本文介绍了ZO-DARTS+，一种可差分神经架构搜索算法，通过一种新的方法通过双层优化生成稀疏概率来提高搜索效率。在五个公开医疗数据集上的实验表明，ZO-DARTS+与最先进的解决方案在准确性上相匹敌，同时将搜索时间减少了30%以上。
URL
https://arxiv.org/abs/2405.03462
