149 citations · 188 across the 11 of their papers we have counts for
12 papers
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy
Caiwen Jiang, Songlin Zhao, Yuzhen Ding +23
Adaptive radiotherapy must account for anatomical changes during treatment, but doing so depends on reliably mapping targets, organs at risk and dose across serial images. Existing…
AI in Proton Therapy Treatment Planning: A Review
Yuzhen Ding, Hongying Feng, Martin Bues +12
Purpose: Proton therapy provides superior dose conformity compared to photon therapy, but its treatment planning is challenged by sensitivity to anatomical changes, setup/range unc…
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy
Yuzhen Ding, Jason Holmes, Hongying Feng +13
Purpose: Intensity-modulated proton therapy (IMPT) offers precise tumor coverage while sparing organs at risk (OARs) in head and neck (H&N) cancer. However, its sensitivity to anat…
Accurate Patient Alignment without Unnecessary Imaging Dose via Synthesizing Patient-specific 3D CT Images from 2D kV Images
Yuzhen Ding, Jason M. Holmes, Hongying Feng +8
In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging(OBI) unavailable. But tumor visibility is constrained due to the projec…
Artificial Intelligence-Facilitated Online Adaptive Proton Therapy Using Pencil Beam Scanning Proton Therapy
Hongying Feng, Jie Shan, Carlos E. Vargas +10
We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with prostate patients. AI-based auto-segmentation tool…
Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report
Jason Holmes, Lian Zhang, Yuzhen Ding +7
Purpose: To introduce the concept of using large language models (LLMs) to re-label structure names in accordance with the American Association of Physicists in Medicine (AAPM) Tas…
Artificial General Intelligence for Radiation Oncology
Chenbin Liu, Zhengliang Liu, Jason Holmes +14
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can…
Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer
Yuzhen Ding, Hongying Feng, Yunze Yang +9
Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The v…
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
Jason Holmes, Zhengliang Liu, Lian Zhang +8
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have…
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
Yuzhen Ding, Nupur Thakur, Baoxin Li
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 imag…
Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks
Nupur Thakur, Yuzhen Ding, Baoxin Li
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 p…
VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection
Yuzhen Ding, Baoxin Li
Topic modeling has found wide application in many problems where latent structures of the data are crucial for typical inference tasks. When applying a topic model, a relatively st…