Zobrazeno 1 - 10
of 44
pro vyhledávání: '"McBeth, Rafe A."'
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. This class of models involves assigning Dirichlet distributions as priors
Externí odkaz:
http://arxiv.org/abs/2410.18461
Autor:
Grandinetti, Jace, McBeth, Rafe
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-specific knowledge. This study introduces ARCoT (Adaptable Retrieval-
Externí odkaz:
http://arxiv.org/abs/2405.11040
Publikováno v:
Computers in Biology and Medicine, Vol. 182, Nov 2024, 109172
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 images of the Open Knowledge-Based Planning Challenge dataset, we fou
Externí odkaz:
http://arxiv.org/abs/2404.17126
Publikováno v:
Computers in Biology and Medicine, vol. 176, June 2024, 108605
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 examining the role of UMAP (Uniform Manifold Approximation and Projectio
Externí odkaz:
http://arxiv.org/abs/2312.10361
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 therapy. This time-consuming and specialized task combines patient imagin
Externí odkaz:
http://arxiv.org/abs/2311.06572
Autor:
Bai, Ti, Balagopal, Anjali, Dohopolski, Michael, Morgan, Howard E., McBeth, Rafe, Tan, Jun, Lin, Mu-Han, Sher, David J., Nguyen, Dan, Jiang, Steve
Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revision. Here, we present a novel concept called artificial intelligence
Externí odkaz:
http://arxiv.org/abs/2107.13465
Publikováno v:
In Computers in Biology and Medicine June 2024 176
Autor:
Mashayekhi, Maryam, Tapia, Itzel Ramirez, Balagopal, Anjali, Zhong, Xinran, Barkousaraie, Azar Sadeghnejad, McBeth, Rafe, Lin, Mu-Han, Jiang, Steve, Nguyen, Dan
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 site-agnostic, 3D dose distribution prediction model using deep lear
Externí odkaz:
http://arxiv.org/abs/2106.07825
Autor:
Nguyen, Dan, Barkousaraie, Azar Sadeghnejad, Bohara, Gyanendra, Balagopal, Anjali, McBeth, Rafe, Lin, Mu-Han, Jiang, Steve
Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinic
Externí odkaz:
http://arxiv.org/abs/2011.00388
Autor:
Nguyen, Dan, McBeth, Rafe, Barkousaraie, Azar Sadeghnejad, Bohara, Gyanendra, Shen, Chenyang, Jia, Xun, Jiang, Steve
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 deep neural networks to generate Pareto optimal dose distributions. The m
Externí odkaz:
http://arxiv.org/abs/1908.05874