# Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans

arXiv: https://arxiv.org/abs/2403.02192 · HTML: https://mlsanity.com/paper/2403.02192/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 58 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [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)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition](https://mlsanity.com/paper/1811.07770/) (2018)
- [The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition](https://mlsanity.com/paper/2402.19344/) (2024)
- [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)
- [Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond](https://mlsanity.com/paper/2401.01219/) (2024)
- [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)
- [A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging](https://mlsanity.com/paper/2303.00175/) (2023)
- [Photorealistic Facial Synthesis in the Dimensional Affect Space](https://mlsanity.com/paper/1811.08004/) (2018)
- [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)
- [BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification](https://mlsanity.com/paper/2310.03485/) (2023)
- [Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks](https://mlsanity.com/paper/1901.07822/) (2019)

## Built on by (most cited first, 30 of 30)

- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit](https://mlsanity.com/paper/2409.17717/) (2024)
- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios](https://mlsanity.com/paper/2506.05372/) (2025)
- [Robust COVID-19 Detection in CT Images with CLIP](https://mlsanity.com/paper/2403.08947/) (2024)
- [Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A feasibility study](https://mlsanity.com/paper/2405.20562/) (2024)
- [Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style](https://mlsanity.com/paper/2410.03848/) (2024)
- [Preserving AUC Fairness in Learning with Noisy Protected Groups](https://mlsanity.com/paper/2505.18532/) (2025)
- [Advancing COVID-19 Detection in 3D CT Scans](https://mlsanity.com/paper/2403.11953/) (2024)
- [Multi-Source COVID-19 Detection via Variance Risk Extrapolation](https://mlsanity.com/paper/2506.23208/) (2025)
- [Robust Facial Reactions Generation: An Emotion-Aware Framework with Modality Compensation](https://mlsanity.com/paper/2407.15798/) (2024)
- [Advancing Lung Disease Diagnosis in 3D CT Scans](https://mlsanity.com/paper/2507.00993/) (2025)
- [Domain Adaptation Using Pseudo Labels for COVID-19 Detection](https://mlsanity.com/paper/2403.11498/) (2024)
- [A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection](https://mlsanity.com/paper/2404.01643/) (2024)
- [Simple 2D Convolutional Neural Network-based Approach for COVID-19 Detection](https://mlsanity.com/paper/2403.11230/) (2024)
- [Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization](https://mlsanity.com/paper/2507.19858/) (2025)
- [Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling](https://mlsanity.com/paper/2507.01564/) (2025)
- [Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans](https://mlsanity.com/paper/2403.11338/) (2024)
- [COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism](https://mlsanity.com/paper/2403.11505/) (2024)
- [High-confidence pseudo-labels for domain adaptation in COVID-19 detection](https://mlsanity.com/paper/2403.13509/) (2024)
- [Multi-Task Learning for Affect Analysis](https://mlsanity.com/paper/2407.00679/) (2024)
- [Emotion Detection through Body Gesture and Face](https://mlsanity.com/paper/2407.09913/) (2024)
- [Stable-Drift: A Patient-Aware Latent Drift Replay Method for Stabilizing Representations in Continual Learning](https://mlsanity.com/paper/2511.22615/) (2025)
- [Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction](https://mlsanity.com/paper/2601.02884/) (2026)
- [A Heterogeneous Ensemble for Multi-Center COVID-19 Classification from Chest CT Scans](https://mlsanity.com/paper/2603.14621/) (2026)
- [Clinical Priors Guided Lung Disease Detection in 3D CT Scans](https://mlsanity.com/paper/2603.15143/) (2026)
- [Vision-Language Model Based Multi-Expert Fusion for CT Image Classification](https://mlsanity.com/paper/2603.15154/) (2026)
- [Robust Multi-Source Covid-19 Detection in CT Images](https://mlsanity.com/paper/2604.03320/) (2026)
- [Robust Fair Disease Diagnosis in CT Images](https://mlsanity.com/paper/2604.09710/) (2026)

## Abstract (arXiv)

The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the 'Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)' Workshop of the 2024 Computer Vision and Pattern Recognition (CVPR) Conference. The Competition is the 4th in the series, following the first three Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 International Conferences respectively. It includes two Challenges on: i) Covid-19 Detection and ii) Covid-19 Domain Adaptation. The Competition use data from COV19-CT-DB database, which is described in the paper and includes a large number of chest CT scan series. Each chest CT scan series consists of a sequence of 2-D CT slices, the number of which is between 50 and 700. Training, validation and test datasets have been extracted from COV19-CT-DB and provided to the participants in both Challenges. The paper presents the baseline models used in the Challenges and the performance which was obtained respectively.