Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Pan, Jinqian"'
Training deep Convolutional Neural Networks (CNNs) presents unique challenges, including the pervasive issue of elimination singularities, consistent deactivation of nodes leading to degenerate manifolds within the loss landscape. These singularities
Externí odkaz:
http://arxiv.org/abs/2409.13154
Generative models can enhance discriminative classifiers by constructing complex feature spaces, thereby improving performance on intricate datasets. Conventional methods typically augment datasets with more detailed feature representations or increa
Externí odkaz:
http://arxiv.org/abs/2409.13116
Autor:
Singh, Pranav, Chukkapalli, Raviteja, Chaudhari, Shravan, Chen, Luoyao, Chen, Mei, Pan, Jinqian, Smuda, Craig, Cirrone, Jacopo
Publikováno v:
Singh, P., Chukkapalli, R., Chaudhari, S. et al. Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification. Sci Rep 14, 10820 (2024)
Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time
Externí odkaz:
http://arxiv.org/abs/2311.10319
Autor:
Singh, Pranav, Chen, Luoyao, Chen, Mei, Pan, Jinqian, Chukkapalli, Raviteja, Chaudhari, Shravan, Cirrone, Jacopo
The task of medical image segmentation presents unique challenges, necessitating both localized and holistic semantic understanding to accurately delineate areas of interest, such as critical tissues or aberrant features. This complexity is heightene
Externí odkaz:
http://arxiv.org/abs/2308.10488
This paper presents a comprehensive survey of low-light image and video enhancement, addressing two primary challenges in the field. The first challenge is the prevalence of mixed over-/under-exposed images, which are not adequately addressed by exis
Externí odkaz:
http://arxiv.org/abs/2212.10772
Point cloud analysis is challenging due to the irregularity of the point cloud data structure. Existing works typically employ the ad-hoc sampling-grouping operation of PointNet++, followed by sophisticated local and/or global feature extractors for
Externí odkaz:
http://arxiv.org/abs/2207.06324
Akademický článek
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Autor:
Xu J; Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States., Talankar S; Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States., Pan J; Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States., Harmon I; Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States., Wu Y; Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States., Fedele DA; Department of Clinical and Health Psychology, University of Florida College of Public Health and Health Professions, Gainesville, FL, United States., Brailsford J; Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States., Fishe JN; Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States.; Department of Emergency Medicine, Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States.
Publikováno v:
JMIR research protocols [JMIR Res Protoc] 2024 Jul 08; Vol. 13, pp. e57981. Date of Electronic Publication: 2024 Jul 08.
