Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914017414479872 |
|---|---|
| author | Reinbigler, Marie Sharma, Rishi Pires, Rafael Brunet, Elisabeth Kermarrec, Anne-Marie Fetita, Catalin |
| author_facet | Reinbigler, Marie Sharma, Rishi Pires, Rafael Brunet, Elisabeth Kermarrec, Anne-Marie Fetita, Catalin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02440 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster Reinbigler, Marie Sharma, Rishi Pires, Rafael Brunet, Elisabeth Kermarrec, Anne-Marie Fetita, Catalin Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition 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. |
| title | Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster |
| topic | Distributed, Parallel, and Cluster Computing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.02440 |