1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classification
Pengfei Gu, Hongxiao Wang, Yejia Zhang +3
Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). % Despite remarkable su…
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…
Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation
Diego Adame, Jose A. Nunez, Fabian Vazquez +5
Convolutional neural network (CNN) and Transformer-based architectures are two dominant deep learning models for polyp segmentation. However, CNNs have limited capability for model…