12 citations · 33 across the 8 of their papers we have counts for
8 papers
Uncertainty quantification in neural network-based glucose prediction for diabetes
Hai Siong Tan, Rafe McBeth
In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three fa…
Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks
Hai Siong Tan
We examine the use of a novel variant of Physics-Informed Neural Networks to predict cosmological parameters from recent supernovae and baryon acoustic oscillations (BAO) datasets.…
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…
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…
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…