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Found 4 papers, 0 papers with code
Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability
The noisy probing dose method showed better generalizability in the 6 outlier cases than the ROI-based and beam mask-based methods with 3D Gamma passing rates (for prostate cancer, targets: 89. 32%$\pm$1. 45% vs. 93. 48%$\pm$1. 51% vs. 96. 79%$\pm$0. 83%, OARs: 85. 87%$\pm$1. 73% vs. 91. 15%$\pm$1. 13% vs. 94. 29%$\pm$1. 01%).
RadOnc-GPT: A Large Language Model for Radiation Oncology
This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods.
Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer
Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions.
