most citedLearning with Geometric Priors in U-Net Variants for Polyp Segmentation

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cs.CV2026

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…

cs.CV20261 cited

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…