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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.
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., Lombaert, H., & Ben Ayed, I. (2022, September). Test-Time Adaptation with Shape Moments for Image Segmentation [Paper]. 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), Singapore. 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., 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
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
Shen, R., Kendinibilir, E., Ben Ayed, I., Lodi, A., Tramontani, A., & Reinelt, G. (2017). An ILP solver for multi-label MRFs with connectivity constraints. (Technical Report n° DS4DM-2017-016). External link
