28 citations · 39 across the 3 of their papers we have counts for
6 papers · 1 filter
A Feasibility Study on Deep Learning Based Individualized 3D Dose Distribution Prediction
Jianhui Ma, Dan Nguyen, Ti Bai +5
Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An optimal dose distribution based on a specific anatomy can be predicted by pre-t…
Boosting radiotherapy dose calculation accuracy with deep learning
Yixun Xing, Ph. D., You Zhang +5
In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Mon…
A Feasibility Study on Deep Learning-Based Radiotherapy Dose Calculation
Yixun Xing, Dan Nguyen, Weiguo Lu +2
Purpose: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and a…
BIRADS Features-Oriented Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis
Erlei Zhang, Stephen Seiler, Mingli Chen +2
Breast ultrasound (US) is an effective imaging modality for breast cancer detection and diagnosis. US computer-aided diagnosis (CAD) systems have been developed for decades and hav…
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
Ana M. Barragan-Montero, Dan Nguyen, Weiguo Lu +4
The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anato…
Towards automated patient data cleaning using deep learning: A feasibility study on the standardization of organ labeling
Timothy Rozario, Troy Long, Mingli Chen +2
Data cleaning consumes about 80% of the time spent on data analysis for clinical research projects. This is a much bigger problem in the era of big data and machine learning in the…