Results 201 to 210 of about 219,753 (266)

Attentional adversarial training for few-shot medical image segmentation without annotations. [PDF]

PLoS One
Awudong B, Li Q, Liang Z, Tian L, Yan J.
europepmc   +1 more source

Progress of metabolomics‐centric multi‐omics research in medicine

iMetaOmics, EarlyView.
The graphical abstract illustrates a holistic roadmap for metabolomics‐centric multi‐omics integration in medical research. The upper panel depicts the technological transition from traditional bulk analysis to high‐resolution single‐cell and spatial methodologies, specifically addressing inherent challenges such as molecular complexity and dynamic ...
Ziyi Wang, Shiwen Deng, Xiaoxu Wang, Xiao Chang, Na Guo, Peng Chen, Hongjun Yang   +6 more
wiley   +1 more source

Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI. [PDF]

J Imaging Inform Med
Javadi M, Sharma R, Tsiamyrtzis P, Webb AG, Leiss E, Tsekos NV.   +5 more
europepmc   +1 more source

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images. [PDF]

IEEE Trans Med Imaging, 2020
Mahmood F, Borders D, Chen RJ, Mckay GN, Salimian KJ, Baras A, Durr NJ.   +6 more
europepmc   +1 more source

Deep learning‐based prediction of cervical lymph node metastasis and genetic alterations from whole‐slide images of thyroid cancer frozen sections

Interdisciplinary Medicine, EarlyView.
Deep learning models accurately predict cervical lymph node metastasis and key genetic mutations (BRAF/TERT) directly from thyroid cancer frozen sections. This AI‐driven pipeline provides a rapid real‐time tool to guide intraoperative surgical decisions, helping to optimize surgical extent and prevent both over‐ and under‐treatment without the need for
Mingxing Qiu, Jinbang Li, Shujing Guo, Danyi Li, Chengyu Lu, Chenglong Zhao, Jiankun He, Hanxi Wang, Qiao Bai, Aihetaimu Aimaier, Zhijian Cen, Rui Mao, Zimo Ye, Jiaxue Zang, Zhining Zhuo, Lijuan Qu, Yueping Liu, Jing Cui, Xiaohong Yao, Xiuwu Bian, Li Liang   +20 more
wiley   +1 more source

Adversarial training collaborating hybrid convolution-transformer network for automatic identification of reactive lymphocytes in peripheral blood. [PDF]

Biomed Opt Express
Mei L, Peng H, Luo P, Jin S, Shen H, He J, Yang W, Ye Z, Sui H, Mei M, Lei C, Xiong B.   +11 more
europepmc   +1 more source

Major Cybersecurity Breaches: Shaping Corporate Cybersecurity Policies and Closing the Gaps

Journal of Corporate Accounting &Finance, EarlyView.
ABSTRACT As digitalization accelerates, cybercrime has intensified in both scale and impact over the past two decades. This study aims to critically examine major cybersecurity events, assess them through the lens of routine activity theory, examine insight from three other established criminological and organizational theories, and address central ...
Laura K. Rickett, Deborah Smith
wiley   +1 more source

Clinical Feasibility of Deep Learning Contrast Synthesis From MR Fingerprinting in Knee Osteoarthritis

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Magnetic Resonance Fingerprinting (MRF) enables rapid quantitative parameter mapping from which synthetic clinical contrast images can be derived using deep learning (DL). Purpose This study evaluates the reliability and interchangeability of MRF‐derived synthetic knee MRI relative to conventional MRI in patients with osteoarthritis.
Mika T. Nevalainen, Olli Nykänen, Jyri Järvinen, Antti Kemppainen, Lasse Räsänen, Victor Casula, Martijn Cloos, Riccardo Lattanzi, Mikko J. Nissi, Miika T. Nieminen   +9 more
wiley   +1 more source

Preparing for Tomorrow's Teamwork: Insights From eSports on How Human Expertise Shapes Training Needs for AI‐Integrated Work

Journal of Organizational Behavior, EarlyView.
ABSTRACT As organizations increasingly adopt human‐AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts.
Caitlin M. Lancaster, Christopher Flathmann, Jennifer Hsu, Nathan J. McNeese, Thomas A. O'Neill, Eduardo Salas   +5 more
wiley   +1 more source

Feasibility and Reproducibility of a Structure‐Guided Deep Learning Model for Automatic Detection of the Standard Sagittal Plane in First‐Trimester Nuchal Translucency Assessment Using 3D Ultrasound

Journal of Ultrasound in Medicine, EarlyView.
Objectives Accurate nuchal translucency (NT) measurement for assessing the risk of fetal genetic abnormalities requires precise acquisition of the mid‐sagittal plane (MSP). However, achieving an appropriate MSP is technically challenging due to anatomical variability and operator dependence inherent in conventional 2‐dimensional (2D) ultrasound.
Hayan Kwon, Hyewon Hur, Hyun Cheol Cho, Yun ji Jung, Suhra Kim, Ja‐Young Kwon   +5 more
wiley   +1 more source