6 papers
From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
Zongyu Li
Latent variables pose a fundamental obstacle to both causal discovery and inference. Local approaches exploiting direct neighborhood relations provide little beyond immediate depen…
Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration
Jiaqi Shang, Haojin Wu, Yinyi Lai +3
Deformable image registration plays a fundamental role in medical image analysis by enabling spatial alignment of anatomical structures across subjects. While recent deep learning-…
Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures
Jordan Jomsky, Kay C. Igwe, Zongyu Li +5
Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological aging and disease risk, yet current approaches rely predominantly on T1-weighted str…
Deep Learning-based MRI Reconstruction with Artificial Fourier Transform Network (AFTNet)
Yanting Yang, Yiren Zhang, Zongyu Li +3
Deep complex-valued neural networks (CVNNs) provide a powerful way to leverage complex number operations and representations and have succeeded in several phase-based applications.…
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning
Yinyi Lai, Anbo Cao, Yuan Gao +3
Early and accurate diagnosis of brain tumors is crucial for improving patient survival rates. However, the detection and classification of brain tumors are challenging due to their…
MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation
Aaron Cao, Zongyu Li, Jordan Jomsky +2
Widely used traditional pipelines for subcortical brain segmentation are often inefficient and slow, particularly when processing large datasets. Furthermore, deep learning models…