News

GitHub4mon
A U-Net-style Encoder-Decoder network with residual blocks and an attention mechanism, adapted for multi-input image relighting tasks.
A U-Net-style Encoder-Decoder network with residual blocks and an attention mechanism, adapted for multi-input image relighting tasks. Resources. Readme Activity. Custom properties. Stars. 0 stars.
IEEE4y
U-Net with Dense Encoder, Residual Decoder and Depth-wise Skip Connections - IEEE Xplore
Abstract: For tasks like medical image segmentation and understanding, U-Net is one of the most prominent convolutional neural networks (CNNs) in recent years. Most of the models for image ...
GitHub5y
GitHub - THUHoloLab/Dense-U-net: Dense-U-net: Dense encoder-decoder network for holographic imaging of 3D particle field
Yufeng Wu, Jiachen Wu, Shangzhong Jin, Liangcai Cao, and Guofan Jin, "Dense-U-net: Dense encoder–decoder network for holographic imaging of 3D particle fields," Optics Communications 493, 126970 (2021 ...
ascopubs.org1mon
Next-generation U-Net Encoder: Decoder for accurate, automated CTC detection from images of peripheral blood nucleated cells stained with EPCAM and DAPI.
We developed a deep-learning pipeline using a U-Net–type encoder–decoder architecture for precise pixel-level CTC discrimination in peripheral blood nucleated cells (PBNCs). This method preserves ...
IEEE4y
Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation
Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation Abstract: Detection of moving objects is a critical component of many computer vision tasks. Recently, deep learning ...
Frontiers11mon
BFNet: a full-encoder skip connect way for medical image segmentation - Frontiers
U-Net is characterized by its encoder-decoder architecture and pioneering skip connections, along with multi-scale features, has served as a fundamental network architecture for many modifications.