The UNet architecture follows the encoder-decoder paradigm. Encoder: The encoder part captures the contextual information from the input image. It consists of repeated applications of convolutional ...

The model consists of: UNet Encoder: Captures spatial features effectively. BiFPN Decoder: Enhances multi-scale feature fusion for better segmentation results. Skip Connections: Allow better gradient ...

In this paper, we propose a deep architecture for semantic segmentation from scratch based on an asymmetry encoder- decoder architecture using Ghost-Net and U-Net which we have called it Ghost-UNet.

After a traumatic brain injury (TBI), there is a risk of intracranial hemorrhage (ICH) occurring, which can have severe consequences such as death or disability. Prompt and accurate diagnosis, ...

On the basis of FCN, a classical encoder–decoder model U-Net is proposed for medical image segmentation tasks, which achieved decent results on various segmentation tasks. Most models for medical ...

The proposed method preserves structural information effectively, but encoder and decoder dropping fail to achieve complete denoising. Originally designed for medical image segmentation, UNet has ...