The performance of UNet is difficult to ameliorate, and the technical challenges of expanding the network size of UNet and extracting detailed information have prevented this architecture from being ...

🧠 Overview This project proposes a wavelet-enhanced UNet architecture for image segmentation tasks, specifically targeting improvements in boundary precision and contour clarity.

In the field of Medical Image Segmentation, deep learning models, especially Convolutional Neural Networks (CNN) and Vision Transformer (ViT) models, have been widely researched and applied in recent ...

In the modern era of medical science, Computed Tomography (CT) image segmentation is an important part that helps to extract the relevant information and supports for meaningful diagnosis. It is ...

Paper Summary: U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI 2015 Olaf Ronneberger, Philipp Fischer, and Thomas Brox [DOI] In this paper, the authors proposed a fully ...

Dilated Residual Network (DRN) has fewer parameters for medical image segmentation, making the architecture less prone to overfitting. Smaller networks with fewer parameters and higher efficiency, ...

RUNet: A Robust UNet Architecture for Image Super-Resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 0–0).

The UNet architecture based on the VGG16 backbone network is capable of extracting multi-level features and is suitable for handling more complex image segmentation tasks, while the multiple ...