Outputs (3)

Self-Regulated Sample Diversity in Large Language Models (2024)
Presentation / Conference Contribution
Liu, M., Frawley, J., Wyer, S., Shum, H. P. H., Uckelman, S. L., Black, S., & Willcocks, C. G. (2024). Self-Regulated Sample Diversity in Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (1891–1899)

MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray (2022)
Presentation / Conference Contribution
Corona-Figueroa, A., Frawley, J., Bond-Taylor, S., Bethapudi, S., Shum, H. P., & Willcocks, C. G. (2022). MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray. . https://doi.org/10.1109/embc48229.2022.9871757

Computed tomography (CT) is an effective med-ical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, inclu... Read More about MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray.

Robust 3D U-Net Segmentation of Macular Holes (2021)
Presentation / Conference Contribution
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2021). Robust 3D U-Net Segmentation of Macular Holes. In A. Pakrashi, E. Rushe, M. H. Z. Bazargani, & B. Mac Namee (Eds.),

Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmentation. We use the 3D U-Net architecture as a basis and experiment with... Read More about Robust 3D U-Net Segmentation of Macular Holes.