Recommended citation: Gaggion, N., Mansilla, L., Milone, D. H., & Ferrante, E. (2021, September). Hybrid graph convolutional neural networks for landmark-based anatomical segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 600-610). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-87193-2_57
Recommended citation: N. Gaggion, L. Mansilla, C. Mosquera, D. H. Milone and E. Ferrante, "Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis," in IEEE Transactions on Medical Imaging, 2022, doi: 10.1109/TMI.2022.3224660. https://ieeexplore.ieee.org/document/9963582
Recommended citation: N. Gaggion, M. Vakalopoulou, D. H. Milone and E. Ferrante, "Multi-Center Anatomical Segmentation with Heterogeneous Labels Via Landmark-Based Models," 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5, doi: 10.1109/ISBI53787.2023.10230691. https://arxiv.org/abs/2211.07395
Recommended citation: Gaggion, N., Matheson, B. A., Xia, Y., Bonazzola, R., Ravikumar, N., Taylor, Z. A., ... & Ferrante, E. (2023). Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI. arXiv preprint arXiv:2311.13706. https://arxiv.org/abs/2311.13706
