1 citations · 1 across the 3 of their papers we have counts for
3 papers
physics.med-ph2025
Deep-learning-based Radiomics on Mitigating Post-treatment Obesity for Pediatric Craniopharyngioma Patients after Surgery and Proton Therapy
Wenjun Yang, Chia-Ho Hua, Tina Davis +2
Purpose: We developed an artificial neural network (ANN) combining radiomics with clinical and dosimetric features to predict the extent of body mass index (BMI) increase after sur…
physics.med-ph2025
Interpreting Convolutional Neural Network Activation Maps with Hand-crafted Radiomics Features on Progression of Pediatric Craniopharyngioma after Irradiation Therapy
Wenjun Yang, Chuang Wang, Tina Davis +3
Purpose: Convolutional neural networks (CNNs) are promising in predicting treatment outcome for pediatric craniopharyngioma while the decision mechanisms are difficult to interpret…
eess.IV2023★ 1 cited
Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors
Shahinur Alam, Jinsoo Uh, Alexander Dresner +2
Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic and radiotherapy (RT) planning tool, offering detailed insights into the anatomy of the human body. The extensive scan…