# Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images

arXiv: https://arxiv.org/abs/2004.05645 · HTML: https://mlsanity.com/paper/2004.05645/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2020.

## Built on by (most cited first, 24 of 24)

- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.05274/) (2021)
- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation](https://mlsanity.com/paper/2206.01793/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors](https://mlsanity.com/paper/2207.00458/) (2022)
- [Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation](https://mlsanity.com/paper/2103.04235/) (2021)
- [D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism](https://mlsanity.com/paper/2102.05210/) (2021)
- [Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images](https://mlsanity.com/paper/2104.09699/) (2021)
- [Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19](https://mlsanity.com/paper/2211.05548/) (2022)
- [Generative Adversarial U-Net for Domain-free Medical Image Augmentation](https://mlsanity.com/paper/2101.04793/) (2021)
- [Global Convolutional Neural Processes](https://mlsanity.com/paper/2109.00691/) (2021)
- [DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem](https://mlsanity.com/paper/2303.05912/) (2023)
- [Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients](https://mlsanity.com/paper/2102.01059/) (2021)
- [Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation](https://mlsanity.com/paper/2109.14818/) (2021)
- [Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation](https://mlsanity.com/paper/2205.09722/) (2022)
- [COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline](https://mlsanity.com/paper/2210.02992/) (2022)
- [DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2105.09683/) (2021)
- [B-line Detection in Lung Ultrasound Videos: Cartesian vs Polar Representation](https://mlsanity.com/paper/2107.12291/) (2021)
- [Training a universal instance segmentation network for live cell images of various cell types and imaging modalities](https://mlsanity.com/paper/2207.14347/) (2022)
- [Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation](https://mlsanity.com/paper/2305.07850/) (2023)
- [Robust Fair Disease Diagnosis in CT Images](https://mlsanity.com/paper/2604.09710/) (2026)
- [Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2608.25693/) (2026)

## Abstract (arXiv)

The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. However, there is still lack of studies on effectively quantifying the lung infection caused by COVID-19. As a basic but challenging task of the diagnostic framework, segmentation plays a crucial role in accurate quantification of COVID-19 infection measured by computed tomography (CT) images. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. Specifically, we use the Aggregated Residual Transformations to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, we validate the efficacy of the proposed algorithm in comparison with other competing methods. Experimental results demonstrate the outstanding performance of our algorithm for automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep leaning-based segmentation tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection in CT images.