用分层渐进分析法，让普通电脑集群几分钟处理完超大图像。
Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster
- 从低分辨率开始逐级聚焦，动态调整分析精度与计算量。
- 相比传统方法减少2.65倍数据处理量，识别准确率不变。
- 适合医疗影像等需高效处理超大图像的研究者使用。
超大图像（gigapixel）分析计算成本高昂。本文提出PyramidAI方法，通过分层渐进策略，从低分辨率开始逐步聚焦感兴趣区域进行高分辨率详细分析，显著降低计算负担。针对自适应分辨率选择的精度-计算权衡问题，我们在生物医学图像数据集Camelyon16上验证了两种策略。结果表明，PyramidAI可将所需处理数据量减少最多2.65倍，同时保持关键区域识别准确率。为实现计算资源普惠，我们利用模拟器评估不同工作节点数量下的最优数据分布与负载均衡算法，并在真实集群中验证：使用12台普通计算机，分析时间从超过一小时缩短至数分钟，为大规模图像分析提供了实用解决方案。
原文摘要 · Abstract (English)
Analyzing gigapixel images is recognized as computationally demanding. In this paper, we introduce PyramidAI, a technique for analyzing gigapixel images with reduced computational cost. The proposed approach adopts a gradual analysis of the image, beginning with lower resolutions and progressively concentrating on regions of interest for detailed examination at higher resolutions. We investigated two strategies for tuning the accuracy-computation performance trade-off when implementing the adaptive resolution selection, validated against the Camelyon16 dataset of biomedical images. Our results demonstrate that PyramidAI substantially decreases the amount of processed data required for analysis by up to 2.65x, while preserving the accuracy in identifying relevant sections on a single computer. To ensure democratization of gigapixel image analysis, we evaluated the potential to use mainstream computers to perform the computation by exploiting the parallelism potential of the approach. Using a simulator, we estimated the best data distribution and load balancing algorithm according to the number of workers. The selected algorithms were implemented and highlighted the same conclusions in a real-world setting. Analysis time is reduced from more than an hour to a few minutes using 12 modest workers, offering a practical solution for efficient large-scale image analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。