U-Net in Medical Imaging: A Practical Pathway for AI Integration in Healthcare

Martin Kryl, Pavel Košan, Petr Včelák, Jana Klečková

2025

Abstract

As AI transforms medical imaging, this paper positions U-Net as a practical and enduring choice for segmentation tasks in constrained clinical environments. Despite rapid advancements in architectures like transformers and hybrid models, U-Net remains highly relevant due to its simplicity, efficiency, and interpretability, particularly in settings with limited computational resources and data availability. By exploring modifications such as residual connections and the Tversky loss function, we argue that incremental refinements to U-Net can bridge the gap between current clinical needs and the potential of more advanced AI tools. This paper advocates for a balanced approach, combining accessible enhancements with hybrid strategies, such as radiologist-informed labeling and advanced preprocessing, to ensure immediate impact while building a foundation for future innovation. U-Net’s adaptability positions it as both a cornerstone of today’s AI integration in healthcare and a stepping stone toward adopting next-generation models.

Paper Citation

in Harvard Style

Kryl M., Košan P., Včelák P. and Klečková J. (2025). U-Net in Medical Imaging: A Practical Pathway for AI Integration in Healthcare. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 828-833. DOI: 10.5220/0013314600003911

in Bibtex Style

@conference{healthinf25,
author={Martin Kryl and Pavel Košan and Petr Včelák and Jana Klečková},
title={U-Net in Medical Imaging: A Practical Pathway for AI Integration in Healthcare},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={828-833},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013314600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - U-Net in Medical Imaging: A Practical Pathway for AI Integration in Healthcare
SN - 978-989-758-731-3
AU - Kryl M.
AU - Košan P.
AU - Včelák P.
AU - Klečková J.
PY - 2025
SP - 828
EP - 833
DO - 10.5220/0013314600003911
PB - SciTePress