Zobrazeno 1 - 10
of 3 740
pro vyhledávání: '"Nejat P"'
Publikováno v:
IEEE Access, 2024
Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-dimensional representations of graphs, has drawn a lot of attention in
Externí odkaz:
http://arxiv.org/abs/2409.20073
Service robots in human-centered environments such as hospitals, office buildings, and long-term care homes need to navigate while adhering to social norms to ensure the safety and comfortability of the people they are sharing the space with. Further
Externí odkaz:
http://arxiv.org/abs/2409.13675
Autor:
Getson, Cristina, Nejat, Goldie
Socially assistive robots are increasingly being used to support the social, cognitive, and physical well-being of those who provide care (healthcare professionals) and those in need of care (older adults). However, the effectiveness of persuasive so
Externí odkaz:
http://arxiv.org/abs/2408.14322
Autor:
Alsaafin, Areej, Nejat, Peyman, Shafique, Abubakr, Khan, Jibran, Alfasly, Saghir, Alabtah, Ghazal, Tizhoosh, H. R.
Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there's a growing demand for efficient retrieva
Externí odkaz:
http://arxiv.org/abs/2404.17704
Mobile robots in unknown cluttered environments with irregularly shaped obstacles often face sensing, energy, and communication challenges which directly affect their ability to explore these environments. In this paper, we introduce a novel deep lea
Externí odkaz:
http://arxiv.org/abs/2402.17904
Tracking of dynamic people in cluttered and crowded human-centered environments is a challenging robotics problem due to the presence of intraclass variations including occlusions, pose deformations, and lighting variations. This paper introduces a n
Externí odkaz:
http://arxiv.org/abs/2402.08774
In unknown cluttered and dynamic environments such as disaster scenes, mobile robots need to perform target-driven navigation in order to find people or objects of interest, while being solely guided by images of the targets. In this paper, we introd
Externí odkaz:
http://arxiv.org/abs/2402.06838
Autor:
Lahr, Isaiah, Alfasly, Saghir, Nejat, Peyman, Khan, Jibran, Kottom, Luke, Kumbhar, Vaishnavi, Alsaafin, Areej, Shafique, Abubakr, Hemati, Sobhan, Alabtah, Ghazal, Comfere, Nneka, Murphee, Dennis, Mangold, Aaron, Yasir, Saba, Meroueh, Chady, Boardman, Lisa, Shah, Vijay H., Garcia, Joaquin J., Tizhoosh, H. R.
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are hi
Externí odkaz:
http://arxiv.org/abs/2401.03271
Autor:
Gonzalez, Ricardo, Nejat, Peyman, Saha, Ashirbani, Campbell, Clinton J. V., Norgan, Andrew P., Lokker, Cynthia
Publikováno v:
Journal of Pathology Informatics. 2023;15:100348
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess
Externí odkaz:
http://arxiv.org/abs/2312.06697
Autor:
Gonzalez, Ricardo, Saha, Ashirbani, Campbell, Clinton J. V., Nejat, Peyman, Lokker, Cynthia, Norgan, Andrew P.
Publikováno v:
Journal of Pathology Informatics 15 (2024) 100347
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges a
Externí odkaz:
http://arxiv.org/abs/2312.03812