N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia. - Md Abdur Rahaman; Daniel H. Mathalon; Hyo Jong Lee; Wenhao Jiang; Bryon A. Mueller; Ole A. Andreassen; Ingrid Agartz; Scott R. Sponheim; Andrew R. Mayer; Julia M. Stephen; Rex E. Jung; Jessica A. Turner; et al. (IEEE Trans. Biomed. Eng., 2020)

Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness. - Md Abdur Rahaman; Jiayu Chen 003; Zening Fu; Noah Lewis; Armin Iraji; Vince D. Calhoun (EMBC, 2021)

Shared sets of correlated polygenic risk scores and voxel-wise grey matter across multiple traits identified via bi-clustering. - Md Abdur Rahaman; Amanda Rodrigue; David C. Glahn; Jessica A. Turner; Vince D. Calhoun (EMBC, 2021)

DCBC_DeepL: Detection and Counting of Blood Cells Employing Deep Learning and YOLOv5 Model. - Md Abdur Rahaman; Md Mamun Ali; Md. Nazmul Hossen; Md. Nayer; Kawsar Ahmed; Francis M. Bui (ICAIDS, 2021)

Statelets: A Novel Multi-Dimensional State-Shape Representation Of Brain Functional Connectivity Dynamics. - Md Abdur Rahaman; Eswar Damaraju; Debbrata Kumar Saha; Vince D. Calhoun; Sergey M. Plis (ISBI, 2021)

A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction. - Giorgio Dolci; Md Abdur Rahaman; Jiayu Chen 003; Kuaikuai Duan; Zening Fu; Anees Abrol; Gloria Menegaz; Vince D. Calhoun (BIBE, 2022)

Performance Analysis between YOLOv5s and YOLOv5m Model to Detect and Count Blood Cells: Deep Learning Approach. - Md Abdur Rahaman; Md Mamun Ali; Kawsar Ahmed; Francis M. Bui; S. M. Hasan Mahmud (ICCA, 2022)

Longitudinal Whole-Brain Functional Network Change Patterns Over A Two-Year Period In The ABCD Data. - Rekha Saha; Debbrata K. Saha; Md Abdur Rahaman; Zening Fu; Vince D. Calhoun (ISBI, 2022)

Two-Dimensional Attentive Fusion for Multi-Modal Learning of Neuroimaging and Genomics Data. - Md Abdur Rahaman; Yash Garg; Armin Iraji; Zening Fu; Jiayu Chen 003; Vince D. Calhoun (MLSP, 2022)

Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals. - Md Abdur Rahaman; Eswar Damaraju; Jessica A. Turner; Theo G. M. van Erp; Daniel H. Mathalon; Jatin G. Vaidya; Bryon A. Mueller; Godfrey D. Pearlson; Vince D. Calhoun (Brain Connect., 2022)

Deep Generative Transfer Learning Predicts Conversion To Alzheimer'S Disease From Neuroimaging Genomics Data. - Giorgio Dolci; Md Abdur Rahaman; Ilaria Boscolo Galazzo; Federica Cruciani; Anees Abrol; Jiayu Chen 003; Zening Fu; Kuaikuai Duan; Gloria Menegaz; Vince D. Calhoun (ICASSP Workshops, 2023)

A Deep Biclustering Framework for Brain Network Analysis. - Md Abdur Rahaman; Zening Fu; Armin Iraji; Vince D. Calhoun (CVPR Workshops, 2024)

SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data. - Md Abdur Rahaman; Zening Fu; Armin Iraji; Vince D. Calhoun (EMBC, 2024)

A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age. - Rekha Saha; Debbrata K. Saha; Md Abdur Rahaman; Zening Fu; Jingyu Liu 001; Vince D. Calhoun (Brain Connect., 2024)

An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease. - Giorgio Dolci; Federica Cruciani; Md Abdur Rahaman; Anees Abrol; Jiayu Chen 003; Zening Fu; Ilaria Boscolo Galazzo; Gloria Menegaz; Vince D. Calhoun (CoRR, 2024)