SOURCE-LINKED INTELLIGENCE

Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At scale, however, generating and handling massive numbers of patches on quickly introduces significant I/O and orchestration overhead, often dominating end-to-end performance. We present a decoupled, I/O-aware pipeline for large-scale WSI embedding extraction that decomposes the workflow into three stages: (1) patch generation and staging, (2) embarrassingly parallel embedding inference, and (3) sharded vector database i

Read original source ↗ Open in workspace

recordType

paper

region

Global