Transformer models improve convolutional neural network limitations, but they fall short of UNet in terms of texture and detail repair. In this paper, we present an effective and efficient ...

UNet is an encoder-decoder architecture with skip connections, designed for segmentation tasks that require high accuracy, especially for medical images. It consists of a contracting path to capture ...

As illustrated in Figure 1, the modified model retains the UNet encoder-decoder framework, utilizing MaxViT to extract deep feature representations that are transferred to the decoder through skip ...

UNet Architecture showing the encoder-decoder structure with skip connections The architecture chosen for this project is a U-Net based model, specifically a variant referred to as GridNetU. This ...

Abstract: The current state-of-the-art works in chest x-ray seg-mentation are based on the U-Net architecture, originally designed and developed for semantic segmentation. The U-Net base model uses a ...

Diffusion models, integral in text-to-video and reference-guided image generation, leverage the UNet architecture, comprising an encoder, bottleneck, and decoder. While past research focused on the ...

To solve the aforementioned problems, in this study, we developed a feature fusion network based on the multi-encoder and single-decoder structure, named MM-UNet, which extracts the corresponding ...