Missing physiological samples create preprocessing uncertainty and uneven client reliability in federated learning. This study proposes Cross-Missingness Stable Trust federated learning (CMST-FL), a two-stage framework that selects an imputer using development patients and then weights local updates from their marginal...
Benachir Rigalma, S. Ziti, M. Belhiah et al.· International Journal of Adv...· 0 citations
This work presents an AI based preprocessing framework that combines advanced imputation methods, including K Nearest Neighbors (KNN), Autoencoder reconstruction, and Generative Adversarial Networks (GAN), with classical baselines such as mean and median imputation to provide robust convergence and stability in federat...
Benachir Rigalma, Hamid Ouhnni, M. Belhiah et al.· International Journal of Adv...· 0 citations
FHIR has successfully catalyzed widespread technical innovation and serves as the foundation for advanced clinical applications, however, a significant translational gap persists and future efforts should expand beyond technical development.
Wafae Abbaoui, S. Ziti, Sara Retal· International Journal of Med...· 0 citations
This study presents an approach for classifying lung diseases using a tailored convolutional neural network (CNN) that combines several chest X-ray (CXR) datasets and applies extensive data augmentation to build a balanced dataset and improve generalization.
Fouad Issouani, Ayyad Maafiri, S. Ziti· Int. J. Online Biomed. Eng.· 0 citations
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