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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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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.
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
Gopinath, K., Desrosiers, C., & Lombaert, H. (2023). Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation. Medical Image Analysis, 90, 102974 (9 pages). External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2022). Learnable Pooling in Graph Convolutional Networks for Brain Surface Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2), 864-876. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2021, September). SegRecon: Learning Joint Brain Surface Reconstruction and Segmentation from Images [Paper]. 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Starsbourg, France. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2020, October). Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation [Paper]. Second International Workshop, UNSURE 2020, and Third International Workshop GRAIL 2020, held in Conjunction with MICCAI 2020, Lima, Peru. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2019, June). Adaptive Graph Convolution Pooling for Brain Surface Analysis [Paper]. 26th Biennal International Conference on Information Processing in Medical Imaging (IPMI 2019), Hong Kong, China. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2019, July). Cortical parcellation via spectral graph convolutions [Abstract]. Medical Imaging with Deep Learning (MIDL 2019), London, UK (5 pages). External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2019). Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation. Medical Image Analysis, 54, 297-305. External link
He, R., Gopinath, K., Desrosiers, C., & Lombaert, H. (2020, April). Spectral Graph Transformer Networks for Brain Surface Parcellation [Paper]. 17th IEEE International Symposium on Biomedical Imaging (ISBI 2020), Iowa City, IA, USA. External link
Reddy, C., Gopinath, K., & Lombaert, H. (2019, July). Brain tumor segmentation using topological loss in convolutional networks [Abstract]. Medical Imaging with Deep Learning (MIDL 2019), London, UK (4 pages). External link
