U Net

U-Net is a convolutional neural network architecture primarily used for image segmentation, aiming to accurately delineate objects or regions of interest within an image. Current research focuses on enhancing U-Net's performance through modifications like incorporating attention mechanisms, transformer blocks, and novel convolutional operations, as well as exploring its application in diverse fields beyond traditional image analysis, such as medical imaging, remote sensing, and audio processing. These advancements improve segmentation accuracy, efficiency, and robustness across various data types and challenging conditions, impacting fields ranging from medical diagnosis to autonomous systems.

Papers

January 5, 2024

UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model
Li Chu, Xiao Bingjia, Yuan Qiping
Transformer Based Model U Net Time Series Data Time Series Prediction Complex Transformer

January 3, 2024

Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels
Jiang You, Arben Cela, René Natowicz, Jacob Ouanounou, Patrick Siarry
Time Series Forecasting U Net Multivariate Time Series Forecasting Input Time Series

December 28, 2023

Image Quality, Uniformity and Computation Improvement of Compressive Light Field Displays with U-Net
Chen Gao, Haifeng Li, Xu Liu, Xiaodi Tan
Image Quality U Net CNN Network Light Field Computational Efficiency Iterative Algorithm Uniformity Metric

December 25, 2023

$\mu$-Net: ConvNext-Based U-Nets for Cosmic Muon Tomography
Li Xin Jed Lim, Ziming Qiu
U Net Tomographic Image

December 23, 2023

Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Haonan Wang, Peng Cao, Xiaoli Liu, Jinzhu Yang, Osmar Zaiane
Medical Image Segmentation Segmentation Task U Net Case Relevance Skip Connection Semantic Gap Attention Calibration

December 21, 2023

Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
Rasha Alshawi, Md Tamjidul Hoque, Md Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall Niles, Ken Prathak, Joe Tom, Jordan Klein, Murtada Mousa, Johny Javier Lopez
Semantic Segmentation U Net Channel Attention Document Boundary Defect Segmentation Feature Splatting

December 19, 2023

December 13, 2023

Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework
Zhuoyao Xin, Christopher Wu, Dong Liu, Chunming Gu, Jia Guo, Jun Hua
Multi Task U Net Computed Tomography Medical Image Synthesis Medical Imaging Task

December 11, 2023

U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation
Seul-Ki Yeom, Julian von Klitzing
U Net Transformer Decoder Efficient Semantic Segmentation Transformer Based UNet Mixture of Attention U MixFormer Outperforms SegFormer Multi Scale Context

December 4, 2023

J-Net: Improved U-Net for Terahertz Image Super-Resolution
Woon-Ha Yeo, Seung-Hwan Jung, Seung Jae Oh, Inhee Maeng, Eui Su Lee, Han-Cheol Ryu
Image Super Resolution U Net THz Image

November 30, 2023

PAUNet: Precipitation Attention-based U-Net for rain prediction from satellite radiance data
P. Jyoteeshkumar Reddy, Harish Baki, Sandeep Chinta, Richard Matear, John Taylor
U Net Multi Spectral Satellite Precipitation Prediction Precipitation Forecasting Precipitation Associated

November 29, 2023

October 30, 2023

Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients
Margerie Huet-Dastarac, Dan Nguyen, Steve Jiang, John Lee, Ana Barragan Montero
Distribution Detection High Uncertainty Anticipation Full State Reconstruction U Net Model Uncertainty Neck Cancer Efficient Uncertainty Estimation

October 24, 2023

Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge
Marek Wodzinski, Henning Müller
High Resolution U Net Encoder Decoder Client Contribution Deep Architecture Self Augmentation Quantitative Segmentation Aorta Segmentation

October 19, 2023

DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation
Guanqun Sun, Yizhi Pan, Weikun Kong, Zichang Xu, Jianhua Ma, Teeradaj Racharak, Le-Minh Nguyen, Junyi Xin
Medical Image Segmentation U Net Spatial Domain Medical Image Segmentation Task Attention Block TransUnet Model

October 17, 2023

October 11, 2023

3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, Matthew Lungren, Lei Xing, Le Lu, Alan Yuille, Yuyin Zhou
Vision Transformer Medical Image Segmentation U Net Multi Organ Segmentation Transformer Encoder Transformer Encoders TransUnet Model

U Net

Papers

UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model

Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels

Image Quality, Uniformity and Computation Improvement of Compressive Light Field Displays with U-Net

$\mu$-Net: ConvNext-Based U-Nets for Cosmic Muon Tomography

Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation

Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation

Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net

Bridging the Gap: Generalising State-of-the-Art U-Net Models to Sub-Saharan African Populations

Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework

U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation

J-Net: Improved U-Net for Terahertz Image Super-Resolution

PAUNet: Precipitation Attention-based U-Net for rain prediction from satellite radiance data

U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery

Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients

Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge

DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation

Zipformer: A faster and better encoder for automatic speech recognition

FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentation

3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers