MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

Abstract

The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.

References in corpus (15)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Attention U-Net: Learning Where to Look for the Pancreas
• Deep Learning Face Representation by Joint Identification-Verification
• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
• COVID-19 Image Data Collection: Prospective Predictions Are the Future
• Richer Convolutional Features for Edge Detection
• COVID-CT-Dataset: A CT Scan Dataset about COVID-19
• Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
• OCNet: Object Context Network for Scene Parsing
• Flattened Convolutional Neural Networks for Feedforward Acceleration
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
• CGNet: A Light-weight Context Guided Network for Semantic Segmentation
• Semantic Edge Detection with Diverse Deep Supervision

Cited by in corpus (10)

• COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
• Detecting COVID-19 from Chest Computed Tomography Scans using AI-Driven Android Application
• Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images
• CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans
• GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation
• Intra-model Variability in COVID-19 Classification Using Chest X-ray Images
• D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism
• Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
• CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans
• A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging