Brain Disorder Classification

Latest papers

18 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Beyond Phenotypic Similarity: Relation Refinement and Selective Population Graph Learning for Multimodal Brain Disorder Diagnosis
Oct 22, 2026Yinyu LanBrain Disorder Classification Multimodal Graph Learning
School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
Uncertainty-Gated SHAP-Dirichlet Refinement for MRI-Based Depression Classification
Oct 19, 2026Phuc Truong Loc Nguyen, Nguyen Truong Toan To, Truong Thanh Hung Nguyen +2Depression Detection Medical Image Classification
FAU Erlangen-Nürnberg, Erlangen, Germany · University of New Brunswick, Fredericton, Canada
MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification
Oct 7, 2026Boseong Kim, Haejun Chung, Ikbeom JangBrain Disorder Classification Neuroimaging
Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea
Separating Diagnosis from Disease Representation: Dual-View EEG Learning with Neural-Dynamics-Guided Deformation
Sep 26, 2026Jiaying Wang, Shouqian Shi, Yutong Chen +5Electroencephalography EEG Decoding
Nanjing University · Tianjin University
HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classification
Sep 17, 2026Proloy Kumar Mondal, Md Kamran Hussin Chowdhury, Hoi Leong LeeMedical Diagnosis Multi-Scale Feature Fusion
Department of Computer Science and Engineering, University of North Texas, Denton, Texas, USA · Inje University, Gimhae-si, Republic of Korea · Faculty of Electronic Engineering and Technology, Universiti Malaysia Perlis, Arau, Malaysia
M2LG-DG: A Multi-modal Local-Global Domain Generalization Framework for Cross-site Major Depressive Disorder Classification
Aug 31, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Depression Detection Brain Disorder Classification
School of Information and Communication Technology, Griffith University, Gold Coast, QLD, 4215, Australia
Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection
Aug 13, 2026Serli Kopar, Sam Gijsen, Abner Hernandez +2Cross-Lingual Transfer Speech Processing
Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany · Tübingen AI Center, University of Tübingen, Tübingen, Germany · Charité–Universitätsmedizin, Department of Psychiatry and Psychotherapy, Berlin, Germany +1
Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis
Jul 8, 2026Yapeng Li, Bo Jiang, Ziyan Zhang +2Medical Diagnosis Hierarchical Representation Learning
School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, China
Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
Jul 6, 2026Md. Taksimul Ahsan Tawhid, Nasif Ahmed Rafe, Alif Tahmid Priyom +1Electroencephalography EEG Decoding
Department of EECE, Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216, Bangladesh
SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs
Jul 2, 2026Yidan Xu, Xiangmin Han, Rundong Xue +1Medical Diagnosis Hypergraph Neural Networks
Hangzhou Dianzi University, China · Tsinghua University, China · Xi’an Jiaotong University, China
A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment
Jun 27, 2026Ali Fatahi, Hoda Zamani, Mohammad H. Nadimi-ShahrakiMedical Diagnosis Medical Image Classification
Department of Computer Engineering, Na.C., Islamic Azad University, Najafabad, 8514143131, Iran · Big Data Research Center, Na. C., Islamic Azad University, Najafabad, 8514143131, Iran · International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Yunlin 640301, Taiwan
Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis
May 19, 2026Giacomo Zanardini, Ryan Moesman, Paul van der Kleij +2Electroencephalography EEG Decoding
Signal Processing Systems, Delft University of Technology, Delft, The Netherlands · Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
May 7, 2026Minheng Chen, Chao Cao, Jing Zhang +2Multi-View Consistency Disentangled Representation Learning
Department of Computer Science and Engineering, University of Texas at Arlington, United States · School of Computing, University of Georgia, United States
How and why does deep ensemble coupled with transfer learning increase performance in bipolar disorder and schizophrenia classification?
Apr 2, 2026Sara Petiton, Antoine Grigis, Benoit Dufumier +1Loss Landscape Deep Ensembles
NeuroSpin/GAIA · NeuroSpin, CEA Saclay, Université Paris-Saclay, France · EPFL, NeuroSpin/GAIA +1
DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI
Mar 27, 2026Qurat Ul Ain, Alptekin Temizel, Soyiba JawedBrain Disorder Classification Classification
Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
Mar 11, 2026Xiaolong Li, Guiliang Guo, Guangqi Wen +6Brain Disorder Classification Contrastive Learning
Computer Science and Engineering, Northeastern University, Shenyang, China · School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China · AiShiWeiLai AI Research, Beijing, China +2
Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification
Mar 10, 2026Wei Liang, Lifang HeBrain Disorder Classification Neuroimaging
Lehigh University, Bethlehem, PA, USA
Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis
Mar 10, 2026Jingfeng Tang, Peng Cao, Guangqi Wen +3Brain Disorder Classification Brain Network Analysis
Computer Science and Engineering, Northeastern University, Shenyang, China · School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China · AiShiWeiLai AI Research, Beijing, China +1

Oct 22, 2026J/K move · Enter open · S save

Beyond Phenotypic Similarity: Relation Refinement and Selective Population Graph Learning for Multimodal Brain Disorder Diagnosis

Yinyu Lan

School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China

Brain Disorder Classification Multimodal Graph Learning Graph Structure Learning Medical Diagnosis fMRI Representation Learning Selective SSMs

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Population graph learning supports brain disorder diagnosis by sharing information across subjects. Its effectiveness, however, depends on whether the relationships guiding this exchange are relevant to diagnosis. Similarity in demographic or acquisition attributes does not necessarily indicate diagnostic agreement, and heterogeneous subjects may require different representation updates within the same population graph. We propose PopDRP, a framework that combines diagnosis-guided relation refinement with hierarchical selective state-space learning. Using only training labels, Contrastive Affinity Refinement calibrates attribute-based affinities according to diagnostic agreement. Neural Graph Rectification then refines these relationships using fused functional-connectivity and non-imaging representations, guided by graph reconstruction and dual-view alignment objectives. On the resulting graph, a Selective Pyramid State-Space backbone integrates input-dependent state updates, graph diffusion, and hierarchical pooling and unpooling to learn population representations at multiple resolutions. Together, these components connect the refinement of inter-subject relationships with selective representation learning over the population graph. Experiments on ABIDE and ADHD-200 under stratified ten-fold cross-validation show improved accuracy and AUC over the compared baselines. Ablation studies further support the contributions of relation refinement and the selective hierarchical backbone to classification performance.