For all publications, please check: https://scholar.google.com/citations?user=1zNm7HsAAAAJ&hl
Publications
2024
Scholarly publications
Cobo, M., Pérez del Barrio, A., Menéndez Fernández-Miranda, P., Sanz Bellón, P., Lloret Iglesias, L., & Silva, W. (2024). Multi-task Learning Approach for Intracranial Hemorrhage Prognosis. In X. Xu, Z. Cui, K. Sun, I. Rekik, & X. Ouyang (Eds.), Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings (pp. 12-21). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15242 LNCS). Springer. https://doi.org/10.1007/978-3-031-73290-4_2
Eijpe, A., Corbetta, V., Chupetlovska, K., Beets-Tan, R., & dos Santos Silva, W. (2025). Enhancing Cross-Modal Medical Image Segmentation Through Compositionality. In Deep Generative Models: DGM4MICCAI (pp. 43-53). (Lecture Notes in Computer Science; Vol. 15224). Springer. Advance online publication. https://doi.org/10.1007/978-3-031-72744-3_5
2023
Scholarly publications
Castro, E. M., Ferreira, P. M., Rebelo, A., Rio-Torto, I., Capozzi, L., Ferreira, M. F., Gonçalves, T., Albuquerque, T., Silva, W., Afonso, C., Sousa, R. G., Cimarelli, C., Daoudi, N., Moreira, G., Yang, H., Hrga, I., Ahmad, J., Keswani, M., & Beco, S. (2023). Fill in the blank for fashion complementary outfit product Retrieval: VISUM summer school competition. Machine Vision and Applications. https://doi.org/10.1007/s00138-022-01359-x
Ferreira, G., Teixeira, M., Belo, R., Silva, W., & Cardoso, J. S. (2023). Deep Learning Strategies For Rare Drug Mechanism of Action Prediction. In 2023 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/ijcnn54540.2023.10191437
Silva, D., Agrotis, G., Beets-Tan, R., Teixeira, L. F., & Silva, W. (2023). Attention-Based Regularisation for Improved Generalisability in Medical Multi-Centre Data. 1412-1417. Paper presented at 2023 International Conference on Machine Learning and Applications (ICMLA). https://doi.org/10.1109/ICMLA58977.2023.00213
Corbetta, V., Beets-Tan, R., & Silva, W. (2023). Interpretability-Guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data. In X. Cao, X. Xu, I. Rekik, Z. Cui, & X. Ouyang (Eds.), Machine Learning in Medical Imaging (1 ed., pp. 330-340). (Lecture Notes in Computer Science; Vol. 14348 ). Springer. https://doi.org/10.1007/978-3-031-45673-2_33
https://dspace.library.uu.nl/bitstream/handle/1874/436455/978-3-031-45673-2_33.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/436455/978-3-031-45673-2_33.pdf?sequence=1
2022
Scholarly publications
Mata, D., Silva, W., & Cardoso, J. S. (2022). Increased Robustness in Chest X-Ray Classification Through Clinical Report-Driven Regularization. In Lecture Notes in Computer Science https://doi.org/10.1007/978-3-031-04881-4_10
Montenegro, H., Silva, W., Gaudio, A., Fredrikson, M., Smailagic, A., & Cardoso, J. S. (2022). Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy. IEEE Access, 10, 28333-28347. https://doi.org/10.1109/ACCESS.2022.3157589
2021
Scholarly publications
Montenegro, H., Silva, W., & Cardoso, J. S. (2021). Towards Privacy-preserving Explanations in Medical Image Analysis. In Interpretable Machine Learning in Healthcare Workshop at ICML
2020
Scholarly publications
Gonçalves, T., dos Santos Silva, W. J., Cardoso, M. J., & Cardoso, J. S. (2020). A novel approach to keypoint detection for the aesthetic evaluation of breast cancer surgery outcomes. Health and Technology. https://doi.org/10.1007/s12553-020-00423-8
Sequeira, A. F., dos Santos Silva, W. J., Ribeiro Pinto, J., Gonçalves, T., & Cardoso, J. S. (2020). Interpretable Biometrics: Should We Rethink How Presentation Attack Detection is Evaluated? In 2020 8th International Workshop on Biometrics and Forensics (IWBF) IEEE. https://doi.org/10.1109/iwbf49977.2020.9107949
Cardoso, J. S., Silva, W., & Cardoso, M. J. (2020). Evolution, current challenges, and future possibilities in the objective assessment of aesthetic outcome of breast cancer locoregional treatment. The Breast, 49, 123-130. https://doi.org/10.1016/j.breast.2019.11.006
2019
Scholarly publications
Gonçalves, T., dos Santos Silva, W. J., & Cardoso, J. S. (2019). Deep Aesthetic Assessment of Breast Cancer Surgery Outcomes. In IFMBE Proceedings (pp. 1967-1983). Springer. https://doi.org/10.1007/978-3-030-31635-8_236
dos Santos Silva, W. J., Castro, E., Cardoso, M. J., Fitzal, F., & Cardoso, J. S. (2019). Deep Keypoint Detection for the Aesthetic Evaluation of Breast Cancer Surgery Outcomes. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) IEEE. https://doi.org/10.1109/isbi.2019.8759331
Silva, W., Fernandes, K., & Cardoso, J. S. (2019). How to produce complementary explanations using an Ensemble Model. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 Article 8852409 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). IEEE. https://doi.org/10.1109/IJCNN.2019.8852409
2018
Scholarly publications
Silva, W., Fernandes, K., Cardoso, M. J., & Cardoso, J. S. (2018). Towards complementary explanations using deep neural networks. In Z. Taylor, M. Reyes, M. J. Cardoso, C. A. Silva, D. Stoyanov, L. Maier-Hein, S. Pereira, S. M. Kia, I. Oguz, B. Landman, A. Martel, E. Duchesnay, T. Lofstedt, A. F. Marquand, & R. Meier (Eds.), Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 133-140). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11038 LNCS). Springer. https://doi.org/10.1007/978-3-030-02628-8_15
Silva, W., Pinto, J. R., & Cardoso, J. S. (2018). A Uniform Performance Index for Ordinal Classification with Imbalanced Classes. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Article 8489327 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). IEEE. https://doi.org/10.1109/IJCNN.2018.8489327