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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…
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…
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…
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…