Search Results for author:
Found 9 papers, 0 papers with code
Mathematical Modeling of the Synergetic Effect between Radiotherapy and Immunotherapy
Achieving effective synergy between radiotherapy and immunotherapy is critical for optimizing tumor control and treatment outcomes.
Using reinforcement learning to design an AI assistantfor a satisfying co-op experience
In this project, we designed an intelligent assistant player for the single-player game Space Invaders with the aim to provide a satisfying co-op experience.
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning
Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics.
Noise Entangled GAN For Low-Dose CT Simulation
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image.
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy
We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks to generate Pareto optimal dose distributions.
Intelligent Inverse Treatment Planning via Deep Reinforcement Learning, a Proof-of-Principle Study in High Dose-rate Brachytherapy for Cervical Cancer
While a treatment planning system can solve the optimization problem with given weights, adjusting the weights for high plan quality is performed by human.
Medical Physics
Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture
The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive critical structures near the target.
Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning
We set up a parameter tuning policy network (PTPN), which maps an CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted.
A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time.
