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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Adiga Vasudeva, S., Dolz, J., & Lombaert, H. (2024). Anatomically-aware uncertainty for semi-supervised image segmentation. Medical Image Analysis, 91, 103011 (10 pages). External link
Adiga V., S., Dolz, J., & Lombaert, H. (2022). Attention-Based Dynamic Subspace Learners for Medical Image Analysis. IEEE Journal of Biomedical and Health Informatics, 26(9), 4599-4610. External link
Adiga Vasudeva, S., Dolz, J., & Lombaert, H. (2022, September). Leveraging Labeling Representations in Uncertainty-Based Semi-supervised Segmentation [Paper]. 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), Singapore. External link
Bateson, M., Kervadec, H., Dolz, J., Lombaert, H., & Ben Ayed, I. (2022). Source-free domain adaptation for image segmentation. Medical Image Analysis, 82, 102617 (12 pages). External link
Bateson, M., Dolz, J., Kervadec, H., Lombaert, H., & Ben Ayed, I. (2021). Constrained Domain Adaptation for Image Segmentation. IEEE Transactions on Medical Imaging, 40(7), 1875-1887. External link
Bateson, M., Kervadec, H., Dolz, J., Lombaert, H., & Ben Ayed, I. (2020, October). Source-Relaxed Domain Adaptation for Image Segmentation [Paper]. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru. External link
Bateson, M., Kervadec, H., Dolz, J., Lombaert, H., & Ben Ayed, I. (2019, October). Constrained Domain Adaptation for Segmentation [Paper]. 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China. External link
Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., & Ben Ayed, I. (2019). HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation. IEEE Transactions on Medical Imaging, 38(5), 1116-1126. External link
Galdrán, A., Anjos, A., Dolz, J., Chakor, H., Lombaert, H., & Ayed, I. B. (2022). State-of-the-art retinal vessel segmentation with minimalistic models. Scientific Reports, 12(1), 6174 (13 pages). Available
Galdrán, A., Dolz, J., Chakor, H., Lombaert, H., & Ben Ayed, I. (2020, October). Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images [Paper]. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru. External link
Galdrán, A., Chelbi, J., Kobi, R., Dolz, J., Lombaert, H., ben Ayed, I., & Chakor, H. (2020). Non-uniform Label Smoothing for Diabetic Retinopathy Grading from Retinal Fundus Images with Deep Neural Networks. Translational Vision Science & Technology, 9(2), 34 (8 pages). External link
Murugesan, B., Vasudeva, S. A., Liu, B., Lombaert, H., Ayed, I. B., & Dolz, J. (2025). Neighbor-aware calibration of segmentation networks with penalty-based constraints. Medical Image Analysis, 103501-103501. External link
Murugesan, B., Adiga Vasudeva, S., Liu, B., Lombaert, H., Ben Ayed, I., & Dolz, J. (2023, October). Trust Your Neighbours: Penalty-Based Constraints for Model Calibration [Paper]. 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Vancouver, Canada. External link
Vasudeva, S. A., Dolz, J., & Lombaert, H. (2025). GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation. The Journal of Machine Learning for Biomedical Imaging, 2(April 2025), 120-134. External link
Vasudeva, S. A., Dolz, J., & Lombaert, H. (2023, July). GeoLS: Geodesic Label Smoothing for Image Segmentation [Paper]. Medical Imaging with Deep Learning (MIDL 2023), Nashville, TN, USA. Published in Proceedings of Machine Learning Research, 227. External link
