1 citations · 1 across the 6 of their papers we have counts for
6 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…
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…
White Light Specular Reflection Data Augmentation for Deep Learning Polyp Detection
Jose Angel Nuñez, Fabian Vazquez, Diego Adame +3
Colorectal cancer is one of the deadliest cancers today, but it can be prevented through early detection of malignant polyps in the colon, primarily via colonoscopies. While this m…
Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8
Fabian Vazquez, Jose Angel Nuñez, Xiaoyan Fu +2
Deep learning methods have demonstrated strong performance in objection tasks; however, their ability to learn domain-specific applications with limited training data remains a sig…