{"doi":"10.3389/fradi.2025.1723272","title":"Ultra-lightweight uncertainty-aware ensemble for large-scale multi-class medical MRI diagnosis","abstract":"This paper introduces an Ultra-Lightweight Uncertainty-Aware Ensemble (UALE) model for large-scale multi-class medical MRI diagnosis, evaluated on the 2024 Benchmark Diagnostic MRI and Medical Imaging Dataset containing 40 classes and 33,616 images. The model integrates five specialized micro-expert networks, each designed to capture distinct MRI features, and combines them using a confidence-weighted ensemble mechanism enhanced with variance-based uncertainty quantification for robust, reliable predictions. With only 0.05M parameters and 0.18 GFLOPs, UALE achieves high efficiency and competitive performance among ultra-lightweight models with an accuracy of 69.1% and an F1 score of 68.3%. Besides lightweight models, the paper offers an extensive analysis and performance comparison with fifteen state-of-the-art models, discusses various datasets, elaborates on uncertainty estimates pertaining to the clinical trustworthiness of the models and possible clinical deployment, and highlights trade-offs and avenues for future work in economically constrained settings. The extreme compactness and reliability of the UALE affords it unique utility in scalable medical diagnostics suitable for low-resource clinical settings and portable imaging devices, such as rural hospitals.","journal":"Frontiers in Radiology","year":2025,"id":5913,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":2,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0514,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-12-19","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":56385,"name":"Fahmid Al Farid","orcid":null,"position":1,"is_corresponding":false},{"id":56386,"name":"Mahe Zabin","orcid":null,"position":2,"is_corresponding":false},{"id":56387,"name":"Jia Uddin","orcid":null,"position":3,"is_corresponding":false},{"id":56388,"name":"Hezerul Abdul Karim","orcid":null,"position":4,"is_corresponding":false},{"id":56384,"name":"Sowad Rahman","orcid":null,"position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}