Zobrazeno 1 - 10
of 6 382
pro vyhledávání: '"McBeth 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:
Ngui, Isaac, McBeth, Courtney, He, Grace, Santos, André Corrêa, Soares, Luciano, Morales, Marco, Amato, Nancy M.
Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to perform each t
Externí odkaz:
http://arxiv.org/abs/2409.12862
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
Autor:
Attali, Amnon, Ashur, Stav, Love, Isaac Burton, McBeth, Courtney, Motes, James, Morales, Marco, Amato, Nancy M.
Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants bias their sampling using various heuristics relat
Externí odkaz:
http://arxiv.org/abs/2404.03133
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
In this work, we present a multi-robot planning framework that leverages guidance about the problem to efficiently search the planning space. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractab
Externí odkaz:
http://arxiv.org/abs/2311.10176
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:
Prajapati, Nikunjkumar, Kunzler, Jakob W., Artusio-Glimpse, Alexandra B., Rotunno, Andrew, Berweger, Samuel, Simons, Matthew T., Holloway, Christopher L., Gardner, Chad M., Mcbeth, Michael S., Younts, Robert A.
Recent advances in Rydberg atom electrometry detail promising applications in radio frequency (RF) communications. Presently, most applications use carrier frequencies greater than 1~GHz where resonant Autler-Townes splitting provides the highest sen
Externí odkaz:
http://arxiv.org/abs/2310.01810
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract The field of bioelectronics is developing exponentially. There is now a drive to interface electronics with biology for the development of new technologies to improve our understanding of electrical forces in biology. This builds on our rece
Externí odkaz:
https://doaj.org/article/ea606d9ca9664bfe9ea74a0c26409de8
