Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

摘要: We describe a suite of tools and methods that form core set capabilities for researchers clinical investigators to evaluate multiple analytical pipelines quantify sensitivity variability the results while conducting large-scale studies in investigative pathology oncology. The overarching objective current investigation is address challenges large data sizes high computational demands. proposed take advantage state-of-the-art parallel machines efficient content-based image searching strategies. content based retrieval (CBIR) algorithms can quickly detect retrieve patches similar query patch using hierarchical analysis approach. component on performance computing carry out consensus clustering 500,000 points shared memory system. Our work demonstrates CBIR be leveraged microscopy images meet clinically salient applications pathology. These technologies enable make more effective use rich informational contained within digitized specimens.