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pro vyhledávání: '"AHN, EUIJOON"'
Remote physiological measurement (RPM) is an essential tool for healthcare monitoring as it enables the measurement of physiological signs, e.g., heart rate, in a remote setting via physical wearables. Recently, with facial videos, we have seen rapid
Externí odkaz:
http://arxiv.org/abs/2406.13136
Autor:
Ahn, Euijoon, Liu, Na, Parekh, Tej, Patel, Ronak, Baldacchino, Tanya, Mullavey, Tracy, Robinson, Amanda, Kim, Jinman
Publikováno v:
JMIR Public Health and Surveillance, Vol 7, Iss 3, p e14837 (2021)
BackgroundOutbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mob
Externí odkaz:
https://doaj.org/article/46f0bcb68b844d59b8ecf8ab304cc375
The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide a detailed view of the surface structures whereas clinical images offer a complementary macroscopic information. The visual
Externí odkaz:
http://arxiv.org/abs/2310.18583
Supervised deep learning methods have achieved considerable success in medical image analysis, owing to the availability of large-scale and well-annotated datasets. However, creating such datasets for whole slide images (WSIs) in histopathology is a
Externí odkaz:
http://arxiv.org/abs/2303.11019
Publikováno v:
Machine Intelligence Research(no. 4, pp. 483-513, 2024)
Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But the application of deep learning in medical image analysis was limited by the scarcity of high-quality anno
Externí odkaz:
http://arxiv.org/abs/2302.05043
Autor:
Zhou, Yusheng, Li, Hao, Liu, Jianan, Kong, Zhengmin, Huang, Tao, Ahn, Euijoon, Lv, Zhihan, Kim, Jinman, Feng, David Dagan
Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for
Externí odkaz:
http://arxiv.org/abs/2301.01732
Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and diagnosis of clinically significant prostate cancer (csPCa). Developing AI-based systems to identify csPCa using bpMRI can transform PCa management by
Externí odkaz:
http://arxiv.org/abs/2212.05808
Autor:
Liu, Jianan, Li, Hao, Huang, Tao, Ahn, Euijoon, Han, Kang, Razi, Adeel, Xiang, Wei, Kim, Jinman, Feng, David Dagan
High-resolution (HR) magnetic resonance imaging is critical in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and costly. Consequently, deep learning-based super-resolution reconstruc
Externí odkaz:
http://arxiv.org/abs/2205.06891
Publikováno v:
In Pattern Recognition November 2024 155
Publikováno v:
In Expert Systems With Applications 15 October 2024 252 Part B