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Peering into the Heart: A Comprehensive Exploration of Semantic Segmentation and Explainable AI on the MnMs-2 Cardiac MRI Dataset

Open Access
|Jan 2025

References

  1. World Heart Federation, “World heart report 2023. Confronting the world’s number one killer,” 2023. [Online]. Available: https://worldheart-federation.org/wp-content/uploads/World-Heart-Report-2023.pdf
  2. J. Edward, J. Banchs, H. Parker, and W. Cornwell, “Right ventricular function across the spectrum of health and disease,” Heart, vol. 109, pp. 349–355, May 2022. https://doi.org/10.1136/heartjnl-2021-320526
  3. C. Martín-Isla et al., “Deep learning segmentation of the right ventricle in cardiac MRI: The M&Ms challenge,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 7, pp. 3302–3313, Jul. 2023. https://doi.org/10.1109/JBHI.2023.3267857
  4. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” arXiv:1411.4038, Nov. 2014. https://doi.org/10.48550/arXiv.1411.4038
  5. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” arXiv:1505.04597, May 2015. https://doi.org/10.48550/arXiv.1505.04597
  6. A. Dosovitskiy et al., “An image is worth 16×16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, Oct. 2020. https://doi.org/10.48550/arXiv.2010.11929
  7. Y.-Z. Li et al., “RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images,” Computer Methods and Programs in Biomedicine, vol. 231, Aug. 2023, Art. no. 107437. https://doi.org/10.1016/j.cmpb.2023.107437
  8. C. Fan, Q. Su, Z. Xiao, H. Su, A. Hou, and B. Luan, “ViT-FRD: A vision transformer model for cardiac MRI image segmentation based on feature recombination distillation,” IEEE Access, vol. 11, pp. 129763–129772, Jan. 2023. https://doi.org/10.1109/access.2023.3302522
  9. K. Borys et al., “Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches,” European Journal of Radiology, vol. 162, May 2023, Art. no. 110786. https://doi.org/10.1016/j.ejrad.2023.110786
  10. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” International Journal of Computer Vision, vol. 128, no. 2, pp. 336–359, Feb. 2020. https://doi.org/10.1007/s11263-019-01228-7
  11. S. Desai and H. G. Ramaswamy, “Ablation-CAM: Visual explanations for deep convolutional network via gradient-free localization,” in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO, USA, Mar. 2020, pp. 972–980. https://doi.org/10.1109/WACV45572.2020.9093360
  12. G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K.-R. Müller, “Layer-wise relevance propagation: An overview,” in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Lecture Notes in Computer Science, W. Samek, G. Montavon, A. Vedaldi, L. Hansen, and K. R. Müller, Eds., vol. 11700, Sep. 2019, pp. 193–209. https://doi.org/10.1007/978-3-030-28954-6_10
  13. S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” arXiv:1705.07874, Nov. 2017. https://doi.org/10.48550/arXiv.1705.07874
  14. A. Janik, J. Dodd, G. Ifrim, K. Sankaran, and K. M. Curran, “Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset,” Medical Imaging 2021: Image Processing, vol. 11596, Feb. 2021. https://doi.org/10.1117/12.2582227
  15. A. Kaur, G. Dong, and A. Basu, “GradXcepUNet: Explainable AI based medical image segmentation,” in Smart Multimedia. ICSM 2022. Lecture Notes in Computer Science, vol 13497. Springer, Cham, Jan. 2022, pp. 174–188. https://doi.org/10.1007/978-3-031-22061-6_13
  16. R. Gipiškis, D. Chiaro, D. Annunziata, and F. Piccialli, “Ablation studies in activation maps for explainable semantic segmentation in industry 4.0,” in IEEE EUROCON 2023 – 20th International Conference on Smart Technologies, Torino, Italy, Jul. 2023, pp. 36–41. https://doi.org/10.1109/eurocon56442.2023.10199094
  17. MICCAI2021, “Multi-disease, multi-view & multi-center. Right ventricular segmentation in cardiac MRI (M&Ms-2).” [Online]. Available: https://www.ub.edu/mnms-2/ Accessed on: Feb. 27, 2024.
  18. K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: surpassing human-level performance on ImageNet classification,” arXiv:1502.01852, Feb. 2015. https://doi.org/10.48550/arxiv.1502.01852
  19. D. Karimi and S. E. Salcudean, “Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks,” IEEE Transactions on Medical Imaging, vol. 39, no. 2, pp. 499–513, Feb. 2020. https://doi.org/10.1109/tmi.2019.2930068
  20. N. Kokhlikyan et al., “Captum: A unified and generic model interpretability library for PyTorch,” arXiv:2009.07896, Sep. 2020. https://doi.org/10.48550/arXiv.2009.07896
  21. UCSF, “How the heart works,” UCSF Department of Surgery, 2024. [Online]. Available: https://surgery.ucsf.edu/condition/how-heart-works
  22. A. Singh, S. Sengupta, and V. Lakshminarayanan, “Explainable deep learning models in medical image analysis,” arXiv:2005.13799, May 2020. https://doi.org/10.48550/arXiv.2005.13799
  23. Ayoob7, “Ayoob7/Peering_in_to_the_heart,” GitHub, 2024. [Online]. Available: https://github.com/Ayoob7/Peering_in_to_the_heart Accessed on: Apr. 28, 2024.
DOI: https://doi.org/10.2478/acss-2025-0002 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Language: English
Page range: 12 - 20
Submitted on: Dec 9, 2024
Accepted on: Jan 7, 2025
Published on: Jan 21, 2025
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Mohamed Ayoob, Oshan Nettasinghe, Vithushan Sylvester, Helmini Bowala, Hamdaan Mohideen, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.