most citedThe Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning

3 citations · 3 across the 1 of their papers we have counts for

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4 papers

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…

physics.med-ph2023

Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet

Rupali Khatun, Soumick Chatterjee, Christoph Bert +6

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously hea…

eess.IV20233 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…

physics.med-ph2023

Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology

Yixing Huang, Ahmed Gomaa, Sabine Semrau +12

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the Unite…