{"doi":"10.1109/tcyb.2020.3005859","title":"Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images","abstract":"Deep-learning methods (especially convolutional neural networks) using structural magnetic resonance imaging (sMRI) data have been successfully applied to computer-aided diagnosis (CAD) of Alzheimer's disease (AD) and its prodromal stage [i.e., mild cognitive impairment (MCI)]. As it is practically challenging to capture local and subtle disease-associated abnormalities directly from the whole-brain sMRI, most of those deep-learning approaches empirically preselect disease-associated sMRI brain regions for model construction. Considering that such isolated selection of potentially informative brain locations might be suboptimal, very few methods have been proposed to perform disease-associated discriminative region localization and disease diagnosis in a unified deep-learning framework. However, those methods based on task-oriented discriminative localization still suffer from two common limitations, that is: 1) identified brain locations are strictly consistent across all subjects, which ignores the unique anatomical characteristics of each brain and 2) only limited local regions/patches are used for model training, which does not fully utilize the global structural information provided by the whole-brain sMRI. In this article, we propose an attention-guided deep-learning framework to extract multilevel discriminative sMRI features for dementia diagnosis. Specifically, we first design a backbone fully convolutional network to automatically localize the discriminative brain regions in a weakly supervised manner. Using the identified disease-related regions as spatial attention guidance, we further develop a hybrid network to jointly learn and fuse multilevel sMRI features for CAD model construction. Our proposed method was evaluated on three public datasets (i.e., ADNI-1, ADNI-2, and AIBL), showing superior performance compared with several state-of-the-art methods in both tasks of AD diagnosis and MCI conversion prediction.","journal":"IEEE Transactions on Cybernetics","year":2020,"id":53115,"datarank":0.7218276533058626,"base_score":4.812184355372417,"endowment":4.812184355372417,"self_citation_contribution":0.7218276533058626,"citation_network_contribution":0.0,"self_endowment_contribution":0.7218276533058626,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":122,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9555,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":270447,"name":"Mingxia Liu","orcid":"0000-0002-0598-5692","position":1,"is_corresponding":false},{"id":270448,"name":"Yongsheng Pan","orcid":"0000-0002-8067-1132","position":2,"is_corresponding":false},{"id":270449,"name":"Dinggang Shen","orcid":"0000-0002-7934-5698","position":3,"is_corresponding":false},{"id":270446,"name":"Chunfeng Lian","orcid":"0000-0002-9319-6633","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T20:44:25.349797Z","pmid":"32721906","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":[]}