The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2608. 07393v1 Announce Type: new Abstract: Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well.
Rhamba is a region‑aware pretraining framework for resting‑state fMRI that combines anatomically guided masking with hybrid Attention‑Mamba architectures. The study pretrained models on the ABIDE dataset using three masking strategies (Any, Majority, Pure) and evaluated four architectural variants, finding that the Mamba‑Attention (MA) hybrid achieved the best average AUROC on downstream schizophrenia and ADHD classification tasks. Explainable AI via Integrated Gradients highlighted that performance depends on the interaction between masking strategy and architecture rather than a single dominant configuration.
arXiv:2606. 00180v1 Announce Type: cross Abstract: Deep learning-based Major Depressive Disorder (MDD) detection using Electroencephalography (EEG) is fundamentally constrained by the "small-sample dilemma.
arXiv:2608.20380v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided di...
arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.
HyperAMS‑Net is a deep learning framework that classifies brain disorders from resting‑state fMRI or structural MRI data. It combines adaptive multi‑scale convolution, hypergraph attention, spatial‑channel attention, and adaptive feature fusion to capture complementary patterns across multiple scales and higher‑order dependencies. Evaluated on ABIDE, REST‑meta‑MDD, and ADNI datasets, it achieves state‑of‑the‑art accuracy and AUC, with ablation studies showing hypergraph attention as the most critical component.