ArticleScientific reports2025
Cross paradigm fusion of federated and continual learning on multilayer perceptron mixer architecture for incremental thoracic infection diagnosis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network.Abdominal radiology (New York) · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
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Authors and funding
2 authors.
Funding
Abstract
Medical imaging is essential in the study of chest virus infections. Due to data sovereignty issues in healthcare, it is essential to employ federated learning to overcome these obstacles. However, obtaining all relevant data at once is challenging, as it is often acquired incrementally. Therefore, addressing continual learning is imperative. To this end, we combined federated learning with continual learning to construct a transnational infectious disease prediction model. This model was applied to the COVID-XRAY and COVID-CT datasets using 3 and 6 clients, respectively, implementing 4 continual learning algorithms across ten different models. Notably, we integrated MLP-Mixer with Learning without Forgetting (LwF) techniques, achieving an accuracy of 54.34%. This demonstrates the effectiveness of our approach in the early detection, sensing, and timely warning of infectious diseases and ultimately builds a multicentre prediction system for future infectious diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.