ArticleProceedings of SPIE--the International Society for Optical Engineering2025
ComPRePS 2.0: Enabling Massive-Scale Distributed Computing on High-Performance Computing Cluster for Histopathological Data Processing.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Fusion Annotator: A Platform for Accelerating Consensus-Driven Ground Truth Generation with AI Assistance.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
System automation and artificial intelligence (AI) have significantly impacted the healthcare sector, leading to the rapid digitization of histological data into gigapixel resolution whole-slide images (WSI). This advancement has fueled the emergence of computational pathology, necessitating high-resolution image processing on a massive scale. To meet this need, a distributed computing environment with extreme scalability and secure architecture is essential for biomedical workloads involving large amounts of private data.. At Computational Microscopic Imaging (CMI) Laboratory, we developed and open-sourced the Computational Renal Pathology Suite (ComPRePS). This image analysis tool integrates histopathological slide visualization, quality evaluation, and AI-driven data analysis for clinical tasks. ComPRePS has already been utilized by the Kidney Precision Medicine Project (KPMP) to process tissue slides and generate data crucial for understanding kidney disease progression. The initial implementation, ComPRePS 1.0, offered a user-friendly interface and AI-driven analysis but faced limitations due to its monolithic architecture, inadequate scalability, and security concerns. These challenges prompted the development of ComPRePS 2.0, which integrates unparalleled computing power from high-performance computing clusters (HPCCs) using University of Florida's (UF) HiperGator-one of the world's largest supercomputers. By leveraging on-demand CPU, GPU and MEM resources, Apptainer-based containerization, and parallel file system access, ComPRePS 2 .0 a chieves unprecedented scalability and improved security. Benchmarking on 920 large-dimensional WSIs demonstrated a 15× performance improvement over its predecessor. ComPRePS 2.0 aims to facilitate truly large-scale automated histopathological slide analysis, benefiting researchers, pathologists, and students worldwide.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.