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Structural cracks don’t stand a chance against this dual-module AI

The YOLOv8 and Swin Transformer dual-module system significantly improves structural crack detection, offering a faster and ...
Frontiers

Deep Learning for Medical Imaging Applications

Artificial Intelligence (AI) is revolutionizing the dynamics of technological advancement in the field of medical imaging, ...
GitHub

OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts [NeurIPS 2025]

OpenWorldSAM pushes the boundaries of SAM2 by enabling open-vocabulary segmentation with flexible language prompts. [2026-1-4]: Demo release: we’ve added simple demos to run OpenWorldSAM on images ...
IEEE

Image Semantic Segmentation Algorithm Based on U-Net

Abstract: The U-Net algorithm, with its unique network structure and excellent performance, has become a classic algorithm in the field of image semantic segmentation. However, there are still some ...
Frontiers

An adaptive fusion of composite attention convolutional neural network for polyp image segmentation

Background: Accurate localization and segmentation of polyp lesions in colonoscopic images are crucial for the early diagnosis of colorectal cancer and treatment planning. However, endoscopic imaging ...
GitHub

VeloxSeg: Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

Overview of VeloxSeg. VeloxSeg employs an encoder-decoder architecture with Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC) on the left, using 1x1 convolution as ...
IEEE

A Reliable Segmentation Model for Limited Medical Image Datasets With Attention Mechanism and Kolmogorov–Arnold Networks

Abstract: The effectiveness and robustness of medical image segmentation algorithms inextricably depends on the quality and quantity of training data. Insufficient data continually restricts model ...