mlsanityData to 7 Oct 2026

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

arXiv 2004.07054 ↗First posted 15 Apr 2020eess.IV cs.CV cs.LGcited by 52

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Abstract (arXiv)

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID-19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID-19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2020.

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Citing papers per year: 2021: 16; 2022: 14; 2023: 9; 2024: 5; 2025: 8.

52 papers.

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  1. 2021Concealed Object Detectioncited by 212
  2. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  3. 2021Mutual Graph Learning for Camouflaged Object Detectioncited by 103
  4. 2022Deep Gradient Learning for Efficient Camouflaged Object Detectioncited by 65
  5. 2022Implicit Motion Handling for Video Camouflaged Object Detectioncited by 60
  6. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  7. 2021Temporal Modulation Network for Controllable Space-Time Video Super-Resolutioncited by 54
  8. 2021Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Networkcited by 42
  9. 2021Deep Texture-Aware Features for Camouflaged Object Detectioncited by 36
  10. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  11. 2024Spider: A Unified Framework for Context-dependent Concept Segmentationcited by 18
  12. 2021TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Imagescited by 12
38 more
  1. 2022COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNscited by 12
  2. 2023Low-Resolution Self-Attention for Semantic Segmentationcited by 12
  3. 2022Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation Systemcited by 11
  4. 2021Visual Transformer with Statistical Test for COVID-19 Classificationcited by 8
  5. 2021Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstructioncited by 5
  6. 2021Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Reviewcited by 5
  7. 2022Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labelscited by 5
  8. 2024An Explainable Non-local Network for COVID-19 Diagnosiscited by 5
  9. 2021Domain adaptation based self-correction model for COVID-19 infection segmentation in CT imagescited by 4
  10. 2021U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Predictioncited by 4
  11. 2022LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noisecited by 4
  12. 2022Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19cited by 4
  13. 2023COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Imagescited by 4
  14. 2025Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generationcited by 4
  15. 2022DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Gradingcited by 3
  16. 2023HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgerycited by 3
  17. 2025Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotationscited by 3
  18. 2022A Deep Ensemble Learning Approach to Lung CT Segmentation for COVID-19 Severity Assessmentcited by 2
  19. 2023D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentationcited by 2
  20. 2024Camouflaged Object Tracking: A Benchmarkcited by 2
  21. 2024Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentationcited by 2
  22. 2025Recurrent Visual Feature Extraction and Stereo Attentions for CT Report Generationcited by 2
  23. 2021CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learningcited by 1
  24. 2021PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Imagescited by 1
  25. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  26. 2021Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Datacited by 1
  27. 2022FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimationcited by 1
  28. 2022Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidancecited by 1
  29. 2023Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalizationcited by 1
  30. 2025CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detectioncited by 1
  31. 2025GAPNet: A Lightweight Framework for Image and Video Salient Object Detection via Granularity-Aware Paradigmcited by 1
  32. 2022Dynamic Message Propagation Network for RGB-D Salient Object Detectioncited by 0
  33. 2022Ensemble CNN models for Covid-19 Recognition and Severity Perdition From 3D CT-scancited by 0
  34. 2023Composite Deep Network with Feature Weighting for Improved Delineation of COVID Infection in Lung CTcited by 0
  35. 20232D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scanscited by 0
  36. 2023Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19cited by 0
  37. 2024Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflagecited by 0
  38. 2025MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality imagescited by 0

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