35 citations · 50 across the 8 of their papers we have counts for
8 papers
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality
Biling Wang, Austen Maniscalco, Ti Bai +8
Purpose: This study presents a Deep Learning (DL)-based quality assessment (QA) approach for evaluating auto-generated contours (auto-contours) in radiotherapy, with emphasis on On…
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Michael Dohopolski, Kai Wang, Biling Wang +5
Prediction uncertainty estimation has clinical significance as it can potentially quantify prediction reliability. Clinicians may trust 'blackbox' models more if robust reliability…
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
Biling Wang, Michael Dohopolski, Ti Bai +10
We evaluated the temporal performance of a deep learning (DL) based artificial intelligence (AI) model for auto segmentation in prostate radiotherapy, seeking to correlate its effi…
Region Specific Optimization (RSO)-based Deep Interactive Registration
Ti Bai, Muhan Lin, Xiao Liang +5
Medical image registration is a fundamental and vital task which will affect the efficacy of many downstream clinical tasks. Deep learning (DL)-based deformable image registration…
Deep High-Resolution Network for Low Dose X-ray CT Denoising
Ti Bai, Dan Nguyen, Biling Wang +1
Low Dose Computed Tomography (LDCT) is clinically desirable due to the reduced radiation to patients. However, the quality of LDCT images is often sub-optimal because of the inevit…
Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm
Ti Bai, Biling Wang, Dan Nguyen +1
Monte Carlo (MC) simulation is considered the gold standard method for radiotherapy dose calculation. However, achieving high precision requires a large number of simulation histor…