Privacy-protecting image classification within the web browser using deep learning models from Zenodo
- Integrating deep learning into clinical workflows for medical image analysis holds promise for improving diagnostic accuracy. However, strict data privacy regulations and the sensitivity of clinical IT infrastructure limit the deployment of cloud-based solutions. This paper introduces WebIPred, a web-based application that loads deep learning models directly within the client’s web browser, protecting patient privacy while maintaining compatibility with clinical IT environments. WebIPred supports the application of pre-trained models published on Zenodo and other repositories, allowing clinicians to apply these models to real patient data without the need for extensive technical knowledge. This paper outlines WebIPred’s model integration system, prediction workflow, and privacy features. Our results show that WebIPred offers a privacy-protecting and flexible application for image classification, only relying on client-side processing. WebIPred combines its strong commitment to dataIntegrating deep learning into clinical workflows for medical image analysis holds promise for improving diagnostic accuracy. However, strict data privacy regulations and the sensitivity of clinical IT infrastructure limit the deployment of cloud-based solutions. This paper introduces WebIPred, a web-based application that loads deep learning models directly within the client’s web browser, protecting patient privacy while maintaining compatibility with clinical IT environments. WebIPred supports the application of pre-trained models published on Zenodo and other repositories, allowing clinicians to apply these models to real patient data without the need for extensive technical knowledge. This paper outlines WebIPred’s model integration system, prediction workflow, and privacy features. Our results show that WebIPred offers a privacy-protecting and flexible application for image classification, only relying on client-side processing. WebIPred combines its strong commitment to data privacy and security with a user-friendly interface that makes it easy for clinicians to integrate AI into their workflows.…
Author: | Florian AuerORCiDGND, Simone MayerORCiD, Frank KramerORCiDGND |
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URN: | urn:nbn:de:bvb:384-opus4-1223935 |
Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/122393 |
ISBN: | 9781643685960OPAC |
ISSN: | 0926-9630OPAC |
ISSN: | 1879-8365OPAC |
Parent Title (English): | Intelligent health systems – from technology to data and knowledge: proceedings of MIE 2025 |
Publisher: | IOS Press |
Place of publication: | Amsterdam |
Editor: | Elisavet Andrikopoulou, Parisis Gallos, Theodoros N. Arvanitis, Rosalynn Austin, Arriel Benis, Ronald Cornet, Panagiotis Chatzistergos, Alexander Dejaco, Linda Dusseljee-Peute, Alaa Mohasseb, Pantelis Natsiavas, Haythem Nakkas, Philip Scott |
Type: | Conference Proceeding |
Language: | English |
Year of first Publication: | 2025 |
Publishing Institution: | Universität Augsburg |
Release Date: | 2025/05/30 |
First Page: | 133 |
Last Page: | 137 |
Series: | Studies in Health Technology and Informatics ; 327 |
DOI: | https://doi.org/10.3233/shti250288 |
Institutes: | Fakultät für Angewandte Informatik |
Fakultät für Angewandte Informatik / Institut für Informatik | |
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für IT-Infrastrukturen für die Translationale Medizinische Forschung | |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Licence (German): | ![]() |