85 citations · 116 across the 12 of their papers we have counts for
12 papers
Evidential Physics-Informed Neural Networks for Scientific Discovery
Hai Siong Tan, Kuancheng Wang, Rafe McBeth
We present the fundamental theory and implementation guidelines underlying Evidential Physics-Informed Neural Network (E-PINN) -- a novel class of uncertainty-aware PINN. It levera…
Evidential Physics-Informed Neural Networks
Hai Siong Tan, Kuancheng Wang, Rafe McBeth
We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quanti…
Automating RT Planning at Scale: High Quality Data For AI Training
Riqiang Gao, Mamadou Diallo, Han Liu +10
Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is o…
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation
Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth
In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. Thi…
From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT
Jace Grandinetti, Rafe McBeth
Large Language Models (LLMs) have achieved remarkable progress, yet their application in specialized fields, such as medical physics, remains challenging due to the need for domain…
Deep Evidential Learning for Radiotherapy Dose Prediction
Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth
In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction. Using medical…
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation
H. S. Tan, Kuancheng Wang, Rafe Mcbeth
In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular ex…
Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments
Kuancheng Wang, Hai Siong Tan, Rafe Mcbeth
The field of Radiation Oncology is uniquely positioned to benefit from the use of artificial intelligence to fully automate the creation of radiation treatment plans for cancer the…
A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision
Ti Bai, Anjali Balagopal, Michael Dohopolski +7
Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revi…
Site-Agnostic 3D Dose Distribution Prediction with Deep Learning Neural Networks
Maryam Mashayekhi, Itzel Ramirez Tapia, Anjali Balagopal +6
Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance. We propose…
A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks
Dan Nguyen, Azar Sadeghnejad Barkousaraie, Gyanendra Bohara +4
Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to becom…
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
Dan Nguyen, Rafe McBeth, Azar Sadeghnejad Barkousaraie +4
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 dee…