Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 8 | |
| Section | Intelligent Computing in Healthcare and Bioinformatics | |
| DOI | https://doi.org/10.1051/itmconf/20268401005 | |
| Published online | 06 April 2026 | |
- L. M. Zintgraf, T. S. Cohen, T. Adel, and M. Welling. Visualizing deep neural network decisions: Prediction difference analysis. In International Conference on Learning Representations, (2017) [Google Scholar]
- D. T. Huff, A. J. Weisman, & R. Jeraj. Interpretation and visualization techniques for deep learning models in medical imaging. Physics in medicine and biology, 66(4), 04TR01. (2021). https://doi.org/10.1088/1361-6560/abcd17 [Google Scholar]
- B. C. Kwon et al., “RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records,” in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 299–309, (2019), doi: 10.1109/TVCG.2018.2865027. [Google Scholar]
- S. Nerella, S. Bandyopadhyay, J. Zhang, M. Contreras, S. Siegel, A. Bumin, et al. Transformers and large language models in healthcare: A review. Artificial intelligence in medicine, 154, 102900. (2024). https://doi.org/10.1016/j.artmed.2024.102900 [Google Scholar]
- A. Esteva, A. Robicquet, B. Ramsundar, et al. A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. (2019). https://doi.org/10.1038/s41591-018-0316-z [Google Scholar]
- P. Rajpurkar, E. Chen, O. Banerjee. et al. AI in health and medicine. Nat Med 28, 31–38 (2022). https://doi-org.eproxy.lib.hku.hk/10.1038/s41591-021-01614-0 [CrossRef] [PubMed] [Google Scholar]
- E. Tjoa and C. Guan, A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI, in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 11, pp. 4793–4813, (2021), doi: 10.1109/TNNLS.2020.3027314. [Google Scholar]
- M. Scherpf, F. Gräßer, H. Malberg. Predicting sepsis with a recurrent neural network using the MIMIC III database, Computers in Biology and Medicine, Volume 113, 103395, ISSN 0010-4825, (2019). https://doi.org/10.1016/j.compbiomed.2019.103395 [Google Scholar]
- H. Strobelt, S. Gehrmann, H. Pfister and A. M. Rush, LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks, in IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 667–676, (2018). doi: 10.1109/TVCG.2017.2744158. [Google Scholar]
- M. Chung, J. B. Won, G. Kim, Y. Kim, & U. Ozbulak. Evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging. In M. Reyes, Z. Chen, M. E. Celebi, & X. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops (pp. 110–120). Springer Nature Switzerland. (2025). https://doi.org/10.1007/978-3-031-77610-6_11 [Google Scholar]
- A. Wollek, R. Graf, S. Čečatka, N. Fink, T. Willem, B. O. Sabel, & T. Lasser. Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification. arXiv.Org. (2023). https://doi.org/10.48550/arxiv.2303.01871 [Google Scholar]
- Z. Salahuddin, H. C. Woodruff, A. Chatterjee, P. Lambin, Transparency of deep neural networks for medical image analysis: A review of interpretability methods, Computers in Biology and Medicine, Volume 140, 105111, ISSN 0010-4825, (2022). https://doi.org/10.1016/j.compbiomed.2021.105111 [Google Scholar]
- D. Badar, J. Abbas, R. Alsini. et al. Transformer attention fusion for fine grained medical image classification. Sci Rep 15, 20655 (2025). https://doi.org/10.1038/s41598-025-07561-x [Google Scholar]
- M. Sushith, A. Lakkshmanan, M. Saravanan. et al. Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection. Sci Rep 15, 7694 (2025). https://doi.org/10.1038/s41598-025-92510-x [Google Scholar]
- P. Zang, T. T. Hormel, J. Wang, Y. Guo, S. T. Bailey et al. Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map. IEEE transactions on bio-medical engineering, 71(1), 14–25. (2024). https://doi.org/10.1109/TBME.2023.3290541 [Google Scholar]
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