Developing a smart and scalable tool for histopathological education—PATe 2.0

Digital microscopy plays a crucial role in pathology education, providing scalable and standardized access to learning resources. In response, we present PATe 2.0, a scalable redeveloped web-application of the former PATe system from 2015. PATe 2.0 was developed using an agile, iterative process and built on a microservices architecture to ensure modularity, scalability, and reliability. It integrates a modern web-based user interface optimized for desktop and tablet use and automates key workflows such as whole-slide image uploads and processing. Performance tests demonstrated that PATe 2.0 significantly reduces tile request times compared to PATe, despite handling larger tiles. The platform supports open formats like DICOM and OpenSlide, enhancing its interoperability and adaptability across institutions. PATe 2.0 represents a robust digital microscopy solution in pathology education enhancing usability, performance, and flexibility. Its design enables future integration of research algorithms and highlights it as a pivotal tool for advancing pathology education and research.

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Publication Details

Journal
Journal of Pathology Informatics
Published
2025-12-05
DOI
https://doi.org/10.1016/j.jpi.2025.100535
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

Developing a smart and scalable tool for histopathological education—PATe 2.0

Vignesh Ramakrishnan, Peter J. Schüffler, Christoph Brochhausen, Georgios Raptis et al.
Journal of Pathology Informatics
AI in cancer detection
article

Developing a smart and scalable tool for histopathological education—PATe 2.0

Vignesh Ramakrishnan, Peter J. Schüffler, Christoph Brochhausen, Georgios Raptis, Lina Winter, Annalena Artinger, Hendrik Böck, Bruno Reible, Jan Albin
article en

Abstract

Digital microscopy plays a crucial role in pathology education, providing scalable and standardized access to learning resources. In response, we present PATe 2.0, a scalable redeveloped web-application of the former PATe system from 2015. PATe 2.0 was developed using an agile, iterative process and built on a microservices architecture to ensure modularity, scalability, and reliability. It integrates a modern web-based user interface optimized for desktop and tablet use and automates key workflows such as whole-slide image uploads and processing. Performance tests demonstrated that PATe 2.0 significantly reduces tile request times compared to PATe, despite handling larger tiles. The platform supports open formats like DICOM and OpenSlide, enhancing its interoperability and adaptability across institutions. PATe 2.0 represents a robust digital microscopy solution in pathology education enhancing usability, performance, and flexibility. Its design enables future integration of research algorithms and highlights it as a pivotal tool for advancing pathology education and research.

Journal of Pathology InformaticsVol. 20
OTH Regensburg (DE), University of Regensburg (DE), Technical University of Munich (DE)
Openalex Percentile: Top 79%
AI in cancer detection
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Developing a smart and scalable tool for histopathological education—PATe 2.0 — Vignesh Ramakrishnan, Peter J. Schüffler, et al. · Journal of Pathology Informatics (2025) | TGRS Research Map | TGRS