Published August 28, 2025 | Version v1
Preprint Open

Standardized Multi-Layer Tissue Maps for Enhanced Artificial Intelligence Integration and Search in Large-Scale Whole Slide Image Archives

Authors/Creators

  • 1. ROR icon Medical University of Graz
  • 2. ROR icon Masaryk University
  • 3. ROR icon Masaryk Memorial Cancer Institute
  • 4. ROR icon Charité - Universitätsmedizin Berlin
  • 5. University Hospital RWTH Aarchen
  • 6. EMPAIA International e.V.
  • 7. European Society of Digital and Integrative Pathology
  • 8. ROR icon BOKU University
  • 9. Alberta Machine Intelligence Institute
  • 10. Medical University Graz
  • 11. ROR icon Graz University of Technology
  • 12. BBMRI-ERIC

Description

A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images can be viewed, analyzed, shared digitally, and are used today for Artificial Intelligence (AI) algorithm development. WSIs are used in a variety of fields, including pathology for diagnosing diseases and oncology for cancer research. They are also utilized in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science.
 
When assembling cohorts for the training or validation of an AI algorithm, it is essential to know what is present on such a WSI. However, there is currently no standard for this metadata, so such selection has mainly been done through manual inspection, which is not suitable for large collections with several million objects.
 
We propose a general framework to generate a 2D index map for WSI and a profiling mechanism for specific application domains. We demonstrate this approach in the field of clinical pathology, using common syntax and semantics to achieve interoperability between different catalogs.

Our approach augments each WSI collection with a detailed tissue map that provides fine-grained information about the WSI content. The tissue map is organized into three layers: source, tissue type, and pathological alterations, with each layer assigning segments of the WSI to specific classes.

We illustrate the advantages and applicability of the proposed standard through specific examples in WSI catalogs, Machine Learning (ML), and graph-based WSI representations.

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Additional details

Related works

Has part
Software: 10.5281/zenodo.16980490 (DOI)

Funding

FWF Austrian Science Fund
Explainable Artificial Intelligence P-32554
European Commission
ONCOSCREEN - A European “shield” against colorectal cancer based on novel, more precise and affordable risk-based screening methods and viable policy pathways 101097036

Dates

Available
2025-08-28