7 papers
Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation
Diego Adame, Fabian Vazquez, Jose A. Nunez +7
CNN- and Transformer-based architectures have achieved strong performance in medical image segmentation, but CNNs are limited in modeling long-range dependencies, while Transformer…
Learning with Geometric Priors in U-Net Variants for Polyp Segmentation
Fabian Vazquez, Jose A. Nuñez, Diego Adame +7
Accurate and robust polyp segmentation is essential for early colorectal cancer detection and for computer-aided diagnosis. While convolutional neural network-, Transformer-, and M…
Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification
Pengfei Gu, Huimin Li, Haoteng Tang +6
Modern deep neural networks have shown remarkable performance in medical image classification. However, such networks either emphasize pixel-intensity features instead of fundament…
Modeling Causal Interactions Across Brain Functional Subnetworks for Population-specific Disease Analysis
Alissen Moreno, Yingying Zhang, Qi Huang +9
Current neuroimaging studies on neurodegenerative diseases and psychological risk factors have been developed predominantly in non Hispanic White cohorts, with other populations ma…
Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks
Islam Akef Ebeid, Haoteng Tang, Pengfei Gu
Introduction Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing drug development. Existing in-silico methods u…
Adapting a Segmentation Foundation Model for Medical Image Classification
Pengfei Gu, Haoteng Tang, Islam A. Ebeid +7
Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to t…