4 papers
Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines
Xiangjian Hou, Chao Qin, Chang Ni +3
Global token mixing, implemented via self-attention or state-space sequence models, has become a popular model design choice for MRI restoration. However, MRI restoration tasks dif…
Depth-Sequence Transformer (DST) for Segment-Specific ICA Calcification Mapping on Non-Contrast CT
Xiangjian Hou, Ebru Yaman Akcicek, Xin Wang +4
While total intracranial carotid artery calcification (ICAC) volume is an established stroke biomarker, growing evidence shows this aggregate metric ignores the critical influence…
Balancing Privacy and Performance for Private Federated Learning Algorithms
Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub +1
Federated learning (FL) is a distributed machine learning (ML) framework where multiple clients collaborate to train a model without exposing their private data. FL involves cycles…
Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation
Shahad Hardan, Hussain Alasmawi, Xiangjian Hou +1
Vestibular schwannoma (VS) is a non-cancerous tumor located next to the ear that can cause hearing loss. Most brain MRI images acquired from patients are contrast-enhanced T1 (ceT1…