7 papers
MAC-Net: A Multi-Task Deep Learning Framework for Modeling Cognitive Function From Task-Based fMRI
Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan +1
Objective cognitive assessment from neural signals supports neurorehabilitation, but individual-level prediction from task-based fMRI (tfMRI) remains difficult because neural featu…
Temporal Graph Learning of Wearable Actigraphy and Sleep Traces for Modelling Adolescent Crystallized Intelligence
Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan +1
Wearable actigraphy offers a scalable, ecologically valid alternative to episodic clinical assessment. However, predicting continuous adolescent crystallized intelligence () f…
Explainable Deep Learning of Resting-State Functional Connectomes Reveals Network Biomarkers of Adolescent Intelligence
Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan +1
Mapping resting-state brain organization to individual differences in cognitive ability remains a major challenge in population neuroinformatics. Although deep learning enables fle…
CG-HAF: An Interpretable Global-Local Lesion-Burden Fusion Framework for Ordinal Acne Severity Grading in Agentic Skincare Support
Muhammad Muhtasim Shahriar, Md. Naimur Asif Borno, Saad Aloteibi +1
Ordinal acne severity grading requires distinguishing visually similar neighboring grades while jointly weighing holistic facial appearance and localized lesion burden - evidence t…
FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification
Muhammad Muhtasim Shahriar, M. M. Golam Hafiz, Saad Aloteibi +1
Cross-site lung histopathology classification must account for stain variation, non-IID client data, missing classes, and the cost of adapting large pathology encoders. This study…
Orthogonal Polynomial Approximation for Matrix Log Normalization in Global Covariance Pooling
Md Rifat Ur Rahman, Md Raihan Khan, Md Sakib Hossain Shovon +2
Global Covariance Pooling (GCP) improves deep networks by capturing second-order feature statistics, and is especially effective for fine-grained recognition. Because covariance ma…
QuanvNeXt: An end-to-end quanvolutional neural network for EEG-based detection of major depressive disorder
Nabil Anan Orka, Ehtashamul Haque, Maftahul Jannat +2
This study presents QuanvNeXt, an end-to-end fully quanvolutional model for EEG-based depression diagnosis. QuanvNeXt incorporates a novel Cross Residual block, which reduces featu…