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

arXiv: https://arxiv.org/abs/2206.04732 · HTML: https://mlsanity.com/paper/2206.04732/

## 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 (most cited first, 18 of 18)

- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond](https://mlsanity.com/paper/1804.10938/) (2018)
- [Analysing Affective Behavior in the First ABAW 2020 Competition](https://mlsanity.com/paper/2001.11409/) (2020)
- [Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework](https://mlsanity.com/paper/2103.15792/) (2021)
- [Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study](https://mlsanity.com/paper/2105.03790/) (2021)
- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges](https://mlsanity.com/paper/2202.10659/) (2022)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition](https://mlsanity.com/paper/1811.07770/) (2018)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset](https://mlsanity.com/paper/1910.01417/) (2019)
- [Photorealistic Facial Synthesis in the Dimensional Affect Space](https://mlsanity.com/paper/1811.08004/) (2018)
- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions](https://mlsanity.com/paper/1811.07771/) (2018)
- [Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm](https://mlsanity.com/paper/1809.04359/) (2018)
- [A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild](https://mlsanity.com/paper/1805.01452/) (2018)
- [A Unified Deep Learning Approach for Prediction of Parkinson's Disease](https://mlsanity.com/paper/1911.10653/) (2019)

## Built on by (most cited first, 20 of 20)

- [ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.01138/) (2022)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors](https://mlsanity.com/paper/2211.14557/) (2022)
- [Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification](https://mlsanity.com/paper/2211.14559/) (2022)
- [COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings](https://mlsanity.com/paper/2206.15073/) (2022)
- [FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis](https://mlsanity.com/paper/2207.01758/) (2022)
- [Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets](https://mlsanity.com/paper/2207.12218/) (2022)
- [COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images](https://mlsanity.com/paper/2312.07580/) (2023)
- [COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images](https://mlsanity.com/paper/2207.00259/) (2022)
- [Representation Learning with Information Theory for COVID-19 Detection](https://mlsanity.com/paper/2207.01437/) (2022)
- [COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline](https://mlsanity.com/paper/2210.02992/) (2022)
- [Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation](https://mlsanity.com/paper/2303.09440/) (2023)
- [PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis](https://mlsanity.com/paper/2206.15069/) (2022)
- [Ensemble CNN models for Covid-19 Recognition and Severity Perdition From 3D CT-scan](https://mlsanity.com/paper/2206.15431/) (2022)
- [Adaptive GLCM sampling for transformer-based COVID-19 detection on CT](https://mlsanity.com/paper/2207.01520/) (2022)
- [Enhancing COVID-19 Severity Analysis through Ensemble Methods](https://mlsanity.com/paper/2303.07130/) (2023)
- [2D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scans](https://mlsanity.com/paper/2303.08740/) (2023)
- [MIA-3DCNN: COVID-19 Detection Based on a 3D CNN](https://mlsanity.com/paper/2303.10738/) (2023)
- [Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration](https://mlsanity.com/paper/2303.16904/) (2023)
- [Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans](https://mlsanity.com/paper/2403.11338/) (2024)

## 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.