3 citations · 3 across the 1 of their papers we have counts for
Showing eess.IVShow all
2 papers · 1 filter
eess.IV2024
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma
Ahmed Gomaa, Yixing Huang, Amr Hagag +16
Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning mod…
eess.IV2023★ 3 cited
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Florian Putz, Johanna Grigo, Thomas Weissmann +13
Background: Tumor segmentation in MRI is crucial in radiotherapy (RT) treatment planning for brain tumor patients. Segment anything (SA), a novel promptable foundation model for au…