57 citations · 101 across the 4 of their papers we have counts for
4 papers
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…
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…
Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy
Thomas Weissmann, Yixing Huang, Stefan Fischer +16
Background: Deep learning (DL)-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still…
Deep learning for brain metastasis detection and segmentation in longitudinal MRI data
Yixing Huang, Christoph Bert, Philipp Sommer +10
Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in rad…