mlsanityData to 14 Oct 2026

AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

arXiv 2206.04732 ↗First posted 9 Jun 2022eess.IV cs.CV cs.LGcited by 20

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Abstract (arXiv)

This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database which is annotated for COVID-19 detction, consisting of about 7,700 3-D CT scans. Part of the database consisting of Covid-19 cases is further annotated in terms of four Covid-19 severity conditions. We have split the database and the latter part of it in training, validation and test datasets. The former two datasets are used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. The baseline approach consists of a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

18 of 49 references.

References · 18

  1. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  2. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  3. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  4. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  5. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  6. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  7. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  8. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
  9. 2018Aff-Wild2: Extending the Aff-Wild Database for Affect Recognitioncited by 81
  10. 2020Deep Transparent Prediction through Latent Representation Analysiscited by 72
  11. 2021MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysiscited by 72
  12. 2019Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Datasetcited by 39
6 more
  1. 2018Photorealistic Facial Synthesis in the Dimensional Affect Spacecited by 33
  2. 2022MixAugment & Mixup: Augmentation Methods for Facial Expression Recognitioncited by 31
  3. 2018A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressionscited by 25
  4. 2018Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigmcited by 20
  5. 2018A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wildcited by 15
  6. 2019A Unified Deep Learning Approach for Prediction of Parkinson's Diseasecited by 4

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Citing papers per year: 2022: 12; 2023: 6; 2024: 2.

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  1. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  2. 2024COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI Systemcited by 23
  3. 2022CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priorscited by 7
  4. 2022Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classificationcited by 7
  5. 2022COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainingscited by 6
  6. 2022FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysiscited by 4
  7. 2022Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNetscited by 4
  8. 2023COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Imagescited by 2
  9. 2022COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Imagescited by 1
  10. 2022Representation Learning with Information Theory for COVID-19 Detectioncited by 1
  11. 2022COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipelinecited by 1
  12. 2023Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentationcited by 1
8 more
  1. 2022PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosiscited by 0
  2. 2022Ensemble CNN models for Covid-19 Recognition and Severity Perdition From 3D CT-scancited by 0
  3. 2022Adaptive GLCM sampling for transformer-based COVID-19 detection on CTcited by 0
  4. 2023Enhancing COVID-19 Severity Analysis through Ensemble Methodscited by 0
  5. 20232D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scanscited by 0
  6. 2023MIA-3DCNN: COVID-19 Detection Based on a 3D CNNcited by 0
  7. 2023Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day explorationcited by 0
  8. 2024Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scanscited by 0