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From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology — arXiv:2608.03508

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03508
License (as archived): CC0-1.0
Archived: 2026-10-31T12:59:42.706Z
SHA-256 of the archived text: 80473196163ae0c940157f12388d717b3684111ffb2f1c951e61f468ad7ad6e0
Stable URL of this page: https://metraipolis.com/documento/dc_7dfe4547fbc2

How to cite

From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology — arXiv:2608.03508 — arXiv (en), https://arxiv.org/abs/2608.03508 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7dfe4547fbc2 (Metraipolis archive dc_7dfe4547fbc2, sha256 80473196163ae0c940157f12388d717b3684111ffb2f1c951e61f468ad7ad6e0)

BibTeX

@misc{dc7dfe4547fbc2,
  title = {From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology — arXiv:2608.03508},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03508},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7dfe4547fbc2; sha256 80473196163ae0c940157f12388d717b3684111ffb2f1c951e61f468ad7ad6e0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03508. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Vision Transformers (ViTs) and their hierarchical variants have achieved strong performance in Computational Pathology (CPath). However, most are pre-trained on single-resolution Whole Slide Images (WSIs), limiting their generalization across arbitrary resolutions. Gigapixel WSIs inherently contain diagnostic patterns at multiple scales, including cellular morphologies, tissue architectures, and global context, mirroring how expert pathologists examine WSIs. We introduce Multi-Resolution Pyramid Transformer (MRPT), a model that hierarchically aggregates multi-resolution information from cellular to tissue and WSI levels. MRPT employs a biologically meaningful Consecutive Cross-Resolution Attention (CCRA) mechanism to capture scale-independent interactions and enforces multi-resolution semantic consistency by aligning embeddings across resolutions, yielding robust and generalizable WSI representations. Pre-trained in a multi-resolution self-supervised manner on 624M patches, 2.4M regions, and 36K WSIs, MRPT learns rich coarse-to-fine histopathology features. Extensive experiments on 34 diverse datasets show that MRPT surpasses recent foundation models and Multimodal Large Language Models (MLLMs) in cancer subtype classification, tissue phenotyping, and Visual Question Answering (VQA) for WSI understanding.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-05 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.03508 — 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 2026-10-31T12:45:33.254Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).