论文标题
在高性能计算环境中进行大脑映射的主动学习管道
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment
论文作者
论文摘要
本文介绍了可扩展的主动学习管道原型，用于利用高性能计算能力的大型大脑映射。它可以对算法结果进行高通量评估，后者在人类审查后将其用于迭代机器学习模型培训。图像处理和机器学习在批处理层进行。使用PMATLAB对图像处理的基准测试表明，可以实现100美元的吞吐量（10,000％），而总处理时间仅在Xeon-G6 CPU上仅增加9％，而在Xeon-E5 CPU上，可以实现22％，这表明可靠的可伸缩性。图像和算法结果是通过将基于浏览器的用户界面的服务层提供的，以进行交互式审核。该管道有可能大大减轻手动注释负担，并改善基于机器学习的大脑映射的整体性能。
This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100$\times$ increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.
