面向脑瘫检测的轻量级神经架构搜索
Lightweight Neural Architecture Search for Cerebral Palsy Detection
AI总结：
针对脑瘫早期检测依赖专家、传统方法性能有限且通用性差的问题，提出采用强化学习更新策略的轻量级神经架构搜索算法，优化得到适配脑瘫检测的网络配置，性能优于集成方法，适合资源受限场景部署。
摘要：
脑瘫（cerebral palsy, CP）是一种神经系统疾病，首次发病于婴儿期或儿童早期，会对运动协调与身体活动造成终身影响。脑瘫是导致儿童残疾的主要原因之一，早期检测对于开展适宜治疗至关重要。但目前这类检测依赖接受过全身运动评估（general movement assessment, GMA）等方法培训的人类专家进行评估，相关服务的可及性不高，在发展中国家尤其如此。传统机器学习方法在脑瘫检测任务上的预测性能有限，且少数现有领域专家开发的方法通常具有数据集特异性，限制了其在原始应用场景之外的适用性。为应对这些挑战，我们提出一种采用强化学习更新策略的神经架构搜索（neural architecture search, NAS）算法，该算法能够高效优化最优架构与超参数组合，从而挖掘最适合脑瘫检测的神经网络配置。在真实世界脑瘫数据集上，我们的方法表现优于该领域其他依赖大型集成模型的方法。由于我们的方法资源需求更低且性能更优，尤其适合在资源受限环境中部署，包括医疗专家及所需诊断工具匮乏的农村或发展中地区。最终得到的模型架构轻量、计算耗时短，可在处理能力有限的设备上部署，减少了对昂贵基础设施的需求，因此能够集成到临床工作流程中，为脑瘫早期诊断提供及时准确的支持。
英文摘要：
The neurological condition known as cerebral palsy (CP) first manifests in infancy or early childhood and has a lifelong impact on motor coordination and body movement. CP is one of the leading causes of childhood disabilities, and early detection is crucial for providing appropriate treatment. However, such detection relies on assessments by human experts trained in methods like general movement assessment (GMA). These are not widely accessible, especially in developing countries. Conventional machine learning approaches offer limited predictive performance on CP detection tasks, and the approaches developed by the few available domain experts are generally dataset-specific, restricting their applicability beyond the context for which these were created. To address these challenges, we propose a neural architecture search (NAS) algorithm applying a reinforcement learning update scheme capable of efficiently optimizing for the best architectural and hyperparameter combination to discover the most suitable neural network configuration for detecting CP. Our method performs better on a real-world CP dataset than other approaches in the field, which rely on large ensembles. As our approach is less resource-demanding and performs better, it is particularly suitable for implementation in resource-constrained settings, including rural or developing areas with limited access to medical experts and the required diagnostic tools. The resulting model's lightweight architecture and efficient computation time allow for deployment on devices with limited processing power, reducing the need for expensive infrastructure, and can, therefore, be integrated into clinical workflows to provide timely and accurate support for early CP diagnosis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
