# Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia

arXiv: https://arxiv.org/abs/2005.02690 · HTML: https://mlsanity.com/paper/2005.02690/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 26 of 26)

- [ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models](https://mlsanity.com/paper/2302.07257/) (2023)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation](https://mlsanity.com/paper/2203.10332/) (2022)
- [Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis](https://mlsanity.com/paper/2212.01108/) (2022)
- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation](https://mlsanity.com/paper/2109.02920/) (2021)
- [Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study](https://mlsanity.com/paper/2104.14506/) (2021)
- [Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation](https://mlsanity.com/paper/2109.03478/) (2021)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection](https://mlsanity.com/paper/2102.11099/) (2021)
- [Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images](https://mlsanity.com/paper/2112.04984/) (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)
- [Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution](https://mlsanity.com/paper/2201.04318/) (2022)
- [Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients](https://mlsanity.com/paper/2102.01059/) (2021)
- [CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation](https://mlsanity.com/paper/2104.05892/) (2021)
- [Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window](https://mlsanity.com/paper/2206.03803/) (2022)
- [3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography](https://mlsanity.com/paper/2206.13385/) (2022)
- [DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images](https://mlsanity.com/paper/2105.06779/) (2021)
- [CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning](https://mlsanity.com/paper/2105.11863/) (2021)
- [CNN-based Local Vision Transformer for COVID-19 Diagnosis](https://mlsanity.com/paper/2207.02027/) (2022)
- [Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images](https://mlsanity.com/paper/2308.05655/) (2023)
- [DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19](https://mlsanity.com/paper/2108.01997/) (2021)
- [Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis](https://mlsanity.com/paper/2111.08606/) (2021)
- [Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis](https://mlsanity.com/paper/2207.01345/) (2022)
- [Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images](https://mlsanity.com/paper/2401.07020/) (2024)

## Abstract (arXiv)

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.