SBC-UNet3+: Classification of Nuclei in Histology Imaging Based on Multi Branch UNET3+ Segmentation Model

Roua Jaafar, Roua Jaafar, Roua Jaafar, Hedi Yazid, Wissem Farhat, Najoua Ben Amara

2025

Abstract

Histological images are crucial for cancer diagnosis and treatment, providing valuable information about cellular structures and abnormalities. Deep learning has emerged as a promising tool to automate the analysis of histological images, especially for tasks like cell segmentation and classification, which aim to improve cancer detection efficiency and accuracy. Existing methods, show promising results in segmentation and classification but are limited in handling overlapping nuclei and boundary delineation. We propose a cell segmentation and classification approach applied to histological images, part of a Content-Based Histopathological Image Retrieval (CBHIR) project. By integrating boundary detection and classification-guided modules, our approach overcomes the limitations of existing methods, enhancing segmentation precision and robustness. Our approach leverages deep learning models and the UNET3+ architecture, comparing its performance with state-of-the-art methods on the PanNuke Dataset (Gamper et al., 2020). Our multitask approach outperforms current models in F1-score and recall, demonstrating its potential for accurate and efficient cancer diagnosis.

Paper Citation

in Harvard Style

Jaafar R., Yazid H., Farhat W. and Ben Amara N. (2025). SBC-UNet3+: Classification of Nuclei in Histology Imaging Based on Multi Branch UNET3+ Segmentation Model. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 601-609. DOI: 10.5220/0013232900003912

in Bibtex Style

@conference{visapp25,
author={Roua Jaafar and Hedi Yazid and Wissem Farhat and Najoua Ben Amara},
title={SBC-UNet3+: Classification of Nuclei in Histology Imaging Based on Multi Branch UNET3+ Segmentation Model},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={601-609},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013232900003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - SBC-UNet3+: Classification of Nuclei in Histology Imaging Based on Multi Branch UNET3+ Segmentation Model
SN - 978-989-758-728-3
AU - Jaafar R.
AU - Yazid H.
AU - Farhat W.
AU - Ben Amara N.
PY - 2025
SP - 601
EP - 609
DO - 10.5220/0013232900003912
PB - SciTePress