Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster

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 evaluated two strategies for balancing accuracy and computational cost in adaptive-resolution selection and validated them on the Camelyon 16 biomedical image dataset. Our results demonstrate that PyramidAI substantially decreases the amount of processed data required for analysis by up to 2.65×, while preserving the accuracy in identifying relevant sections on a single computer. To advance democratization of gigapixel image analysis, we evaluated whether mainstream computers can perform the computation by exploiting the inherent parallelism in the approach. Using a simulator, we estimated the best data distribution and load balancing algorithm according to the number of workers. We implemented the selected algorithms and confirmed that they led to the same conclusions when applied 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.