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Affiliation

Institut Supérieur d’Electronique de Paris (ISEP), 10 rue de Vanves, Issy-les-Moulineaux, 92130, France

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1 Papers

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

Topics: Segmentation and Grouping; Features Extraction; Object Detection and Localization; Medical Image Applications; Multi-task learning

Authors: Roua Jaafar ^{1

;

2

;

3} , Hedi Yazid ³ , Wissem Farhat ¹ and Najoua Ben Amara ¹

Affiliations: ¹ Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS - Laboratory of Advanced Technology and Intelligent Systems, 4023, Sousse, Tunisia , ² Université de Sousse, Institut Supérieur d’Informatique et des Technologies de Communication de Sousse, 4011, Sousse, Tunisia , ³ Institut Supérieur d’Electronique de Paris (ISEP), 10 rue de Vanves, Issy-les-Moulineaux, 92130, France

Digital Object Identifier: 10.5220/0013232900003912

Proceedings of VISAPP 2025, ISBN: 978-989-758-728-3

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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.