Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans (2403.02192v2)

Published 4 Mar 2024 in eess.IV and cs.CV

Abstract: 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (47)
  1. “A deep learning algorithm using ct images to screen for corona virus disease (covid-19),” European radiology, pp. 1–9, 2021.
  2. “Mia-cov19d: Covid-19 detection through 3-d chest ct image analysis,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 537–544.
  3. “Ai-mia: Covid-19 detection and severity analysis through medical imaging,” in Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII. Springer, 2023, pp. 677–690.
  4. “Ai-enabled analysis of 3-d ct scans for diagnosis of covid-19 & its severity,” in 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023, pp. 1–5.
  5. “A deep neural architecture for harmonizing 3-d input data analysis and decision making in medical imaging,” Neurocomputing, vol. 542, pp. 126244, 2023.
  6. “A large imaging database and novel deep neural architecture for covid-19 diagnosis,” in 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, 2022, pp. 1–5.
  7. “Deep transparent prediction through latent representation analysis,” arXiv preprint arXiv:2009.07044, 2020.
  8. “Transparent adaptation in deep medical image diagnosis,” in International Workshop on the Foundations of Trustworthy AI Integrating Learning, Optimization and Reasoning. Springer, 2020, pp. 251–267.
  9. “Predicting parkinson’s disease using latent information extracted from deep neural networks,” in 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019, pp. 1–8.
  10. “Data-driven covid-19 detection through medical imaging,” in 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023, pp. 1–5.
  11. “Deep neural architectures for prediction in healthcare,” Complex & Intelligent Systems, vol. 4, no. 2, pp. 119–131, 2018.
  12. “Adaptation and contextualization of deep neural network models,” in Computational Intelligence (SSCI), 2017 IEEE Symposium Series on. IEEE, 2017, pp. 1–8.
  13. “Bottom-up spatiotemporal visual attention model for video analysis,” IET Image Processing, vol. 1, no. 2, pp. 237–248, 2007.
  14. “A snake model for object tracking in natural sequences,” Signal processing: image communication, vol. 19, no. 3, pp. 219–238, 2004.
  15. “Face detection in color images and video sequences,” in 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No. 00CH37099). IEEE, 2000, vol. 2, pp. 498–502.
  16. “Optimal filter banks for signal reconstruction from noisy subband components,” IEEE transactions on signal processing, vol. 44, no. 2, pp. 212–224, 1996.
  17. “Intelligent one-stop-shop travel recommendations using an adaptive neural network and clustering of history,” Information Technology & Tourism, vol. 6, no. 3, pp. 181–193, 2003.
  18. “Facernet: a facial expression intensity estimation network,” arXiv preprint arXiv:2303.00180, 2023.
  19. “Ai-mia: Covid-19 detection and severity analysis through medical imaging,” in European Conference on Computer Vision. Springer, 2022, p. 677–690.
  20. “Distribution matching for multi-task learning of classification tasks: a large-scale study on faces & beyond,” arXiv preprint arXiv:2401.01219, 2024.
  21. “Medical image segmentation: A review of modern architectures,” in Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII. Springer, 2023, pp. 691–708.
  22. “Interweaving deep learning and semantic techniques for emotion analysis in human-machine interaction,” in 2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). IEEE, 2015, pp. 1–6.
  23. “Va-stargan: Continuous affect generation,” in International Conference on Advanced Concepts for Intelligent Vision Systems. Springer, 2020, pp. 227–238.
  24. “On line emotion detection using retrainable deep neural networks,” in Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016, pp. 1–8.
  25. “Photorealistic facial synthesis in the dimensional affect space,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0–0.
  26. “A multi-task learning & generation framework: Valence-arousal, action units & primary expressions,” arXiv preprint arXiv:1811.07771, 2018.
  27. “Aff-wild2: Extending the aff-wild database for affect recognition,” arXiv preprint arXiv:1811.07770, 2018.
  28. “Training deep neural networks with different datasets in-the-wild: The emotion recognition paradigm,” in 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018, pp. 1–8.
  29. “Assessment of parkinson’s disease based on deep neural networks,” in International Conference on Engineering Applications of Neural Networks. Springer, 2017, pp. 391–403.
  30. “Machine learning for neurodegenerative disorder diagnosis—survey of practices and launch of benchmark dataset,” International Journal on Artificial Intelligence Tools, vol. 27, no. 03, pp. 1850011, 2018.
  31. “Deep neural network augmentation: Generating faces for affect analysis,” International Journal of Computer Vision, pp. 1–30, 2020.
  32. “Exploiting multi-cnn features in cnn-rnn based dimensional emotion recognition on the omg in-the-wild dataset,” IEEE Transactions on Affective Computing, 2020.
  33. Dimitrios Kollias, “Multi-label compound expression recognition: C-expr database & network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 5589–5598.
  34. “Aff-wild: Valence and arousal ‘in-the-wild’challenge,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on. IEEE, 2017, pp. 1980–1987.
  35. “Recognition of affect in the wild using deep neural networks,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on. IEEE, 2017, pp. 1972–1979.
  36. “Expression, affect, action unit recognition: Aff-wild2, multi-task learning and arcface,” arXiv preprint arXiv:1910.04855, 2019.
  37. “Analysing affective behavior in the first abaw 2020 competition,” in 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)(FG), 2020, pp. 794–800.
  38. “Analysing affective behavior in the second abaw2 competition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 3652–3660.
  39. “Affect analysis in-the-wild: Valence-arousal, expressions, action units and a unified framework,” arXiv preprint arXiv:2103.15792, 2021.
  40. “Distribution matching for heterogeneous multi-task learning: a large-scale face study,” arXiv preprint arXiv:2105.03790, 2021.
  41. Dimitrios Kollias, “Abaw: Valence-arousal estimation, expression recognition, action unit detection & multi-task learning challenges,” arXiv preprint arXiv:2202.10659, 2022.
  42. “Face behavior a la carte: Expressions, affect and action units in a single network,” arXiv preprint arXiv:1910.11111, 2019.
  43. “Abaw: Valence-arousal estimation, expression recognition, action unit detection & emotional reaction intensity estimation challenges,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 5888–5897.
  44. “The 6th affective behavior analysis in-the-wild (abaw) competition,” arXiv preprint arXiv:2402.19344, 2024.
  45. “Mixaugment & mixup: Augmentation methods for facial expression recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 2367–2375.
  46. “Deep learning-based detection for covid-19 from chest ct using weak label,” MedRxiv, 2020.
  47. “Serial quantitative chest ct assessment of covid-19: a deep learning approach,” Radiology: Cardiothoracic Imaging, vol. 2, no. 2, pp. e200075, 2020.
Citations (21)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now