Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation

Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation

Research Background and Motivation Medical image segmentation is of great significance in the image analysis of anatomical structures and lesion areas, as well as in clinical diagnosis. However, existing fully supervised learning methods rely on a large amount of annotated data, and obtaining pixel-level annotated data for medical images is costly ...

TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network

TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network

TGFuse: A Transformer and Generative Adversarial Network-Based Method for Infrared and Visible Image Fusion Background Introduction With the development of imaging devices and analysis methods, multimodal visual data is rapidly emerging, with many practical applications. In these applications, image fusion plays a significant role in helping the hu...