![[2508.21418] From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2508.21418] From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology
Summary
This article presents a framework for creating standardized multi-layer tissue maps as metadata for AI in digital pathology, enhancing the usability of Whole Slide Images (WSIs) for research and diagnosis.
Why It Matters
The integration of standardized tissue maps into digital pathology addresses the lack of uniform metadata, facilitating better AI training and validation processes. This advancement is crucial for improving diagnostic accuracy and accelerating cancer research, making it highly relevant in the fields of medicine and machine learning.
Key Takeaways
- Proposes a framework for generating AI-ready metadata from WSIs.
- Standardized tissue maps enhance interoperability and search capabilities.
- Facilitates the assembly of high-quality datasets for AI applications in pathology.
- Addresses the challenges of manual inspection in large WSI collections.
- Supports advancements in cancer research and diagnostic processes.
Computer Science > Computer Vision and Pattern Recognition arXiv:2508.21418 (cs) [Submitted on 29 Aug 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology Authors:Gernot Fiala, Markus Plass, Robert Harb, Peter Regitnig, Kristijan Skok, Wael Al Zoughbi, Carmen Zerner, Paul Torke, Michaela Kargl, Heimo Müller, Tomas Brazdil, Matej Gallo, Jaroslav Kubín, Roman Stoklasa, Rudolf Nenutil, Norman Zerbe, Andreas Holzinger, Petr Holub View a PDF of the paper titled From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology, by Gernot Fiala and 17 other authors View PDF Abstract: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 are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no stan...