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Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction — arXiv:2608.27278

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27278
License (as archived): CC0-1.0
Archived: 2026-10-17T15:28:25.142Z
SHA-256 of the archived text: 0439e2293d87d901ebec92327dbe5b813f3307294239df45244581296be8acb4
Stable URL of this page: https://metraipolis.com/documento/dc_8a412da453dc

How to cite

Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction — arXiv:2608.27278 — arXiv (en), https://arxiv.org/abs/2608.27278 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8a412da453dc (Metraipolis archive dc_8a412da453dc, sha256 0439e2293d87d901ebec92327dbe5b813f3307294239df45244581296be8acb4)

BibTeX

@misc{dc8a412da453dc,
  title = {Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction — arXiv:2608.27278},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27278},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8a412da453dc; sha256 0439e2293d87d901ebec92327dbe5b813f3307294239df45244581296be8acb4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27278. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 ingestion. This design isolates data movement from compute, enabling efficient patch delivery, scalable multi-node inference with minimal communication. The resulting system produces a distributed vector database where embeddings are persistently coupled with rich metadata (e.g., patient, slide, and patch attributes), enabling efficient filtering, retrieval, and downstream reuse. This representation database is compact and reusable for tasks such as retrieval, classification, and few-shot learning, particularly benefiting low-resource environments. We show that decoupling I/O, computation, and ingestion enables high-throughput WSI embedding extraction at scale. By characterizing the scaling envelope, we demonstrate that storage dominates beyond moderate concurrency, reframing WSI embedding extraction as a data-centric systems problem rather than a purely compute-bound workload.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-28 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.27278 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T15:17:44.898Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).