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Madni, H.A.* ; Umer, R.M. ; Zottin, S.* ; Marr, C. ; Foresti, G.L.*

FL-W3S: Cross-domain federated learning for weakly supervised semantic segmentation of white blood cells.

Int. J. Med. Inform. 195:105806 (2025)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
BACKGROUND: Segmentation models for clinical data experience severe performance degradation when trained on a single client from one domain and distributed to other clients from different domain. Federated Learning (FL) provides a solution by enabling multi-party collaborative learning without compromising the confidentiality of clients' private data. METHODS: In this paper, we propose a cross-domain FL method for Weakly Supervised Semantic Segmentation (FL-W3S) of white blood cells in microscopic images. We perform model training on multiple clients with different data distributions to obtain a global aggregated model using only image-level class labels for semantic segmentation of white blood cells. A multi-class token transformer model learns the relationship between patch tokens and class tokens during collaborative learning and generates class-specific localization maps for mask predictions. To rectify the localization maps, we use patch-level pairwise affinity obtained from patch-to-patch transformer attention. RESULTS: We evaluate performance of the proposed semantic segmentation method on two different datasets of white blood cells from different domains. Our experimental results show that for two datasets, there is 2.56% and 1.39% increase in performance of the proposed method over existing state-of-the-art methods. CONCLUSION: The combination of federated learning for collaborative model training while preserving data privacy, alongside white blood cell segmentation techniques for precise cell identification, enhances diagnostic accuracy and personalized treatment strategies in clinical applications, particularly in hematology and pathology. More specifically, it involves isolating white blood cell from blood smear for further analysis such as automated blood cell counting, morphological analysis, cell classification, disease diagnosis and monitoring.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Federated Learning ; Transformer Attention ; Weakly Supervised Semantic Segmentation ; White Blood Cell
ISSN (print) / ISBN 1386-5056
e-ISSN 1872-8243
Zeitschrift International Journal of Medical Informatics
Quellenangaben Band: 195, Heft: , Seiten: , Artikelnummer: 105806 Supplement: ,
Verlag Elsevier
Verlagsort Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Förderungen European Union-Next Generation EU
(Component 2, Investment 1.5)
European Research Council (ERC) under the European Union's Horizon 2020 re-search and innovation program
Hightech Agenda Bayern