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Found 6 papers, 0 papers with code
Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report
Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.
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.
Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks
Though deep neural networks (DNNs) have shown superiority over other techniques in major fields like computer vision, natural language processing, robotics, recently, it has been proven that they are vulnerable to adversarial attacks.
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
Researches have shown that deep neural networks are vulnerable to malicious attacks, where adversarial images are created to trick a network into misclassification even if the images may give rise to totally different labels by human eyes.
VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection
When applying a topic model, a relatively standard pre-processing step is to first build a vocabulary of frequent words.
