Results 221 to 230 of about 5,920,185 (311)

GR-AttNet: Robotic grasping with lightweight spatial attention mechanism. [PDF]

PLoS One
Zhou S, Liu Z, Li C, Chen D, Liu S, Liu Y.   +5 more
europepmc   +1 more source

Automated Hand Flexor Tendon–Thickness Measurement in Systemic Sclerosis

Arthritis Care &Research, EarlyView.
Objective Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time‐consuming process prone to interobserver variability.
Mark Greveling, Ties van het Erve, Yvette van de Berg, Simon Mastbergen, Julia Spierings   +4 more
wiley   +1 more source

Channel-spatial attention modules in convolutional neural networks for image classification. [PDF]

Sci Rep
Zolfaghari M, Saniee Abadeh M, Sajedi H.
europepmc   +1 more source

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

Arthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen, Yasuo Nagafuchi, Kristina Lend, Seyam Alemyar, Herman Kasper Glas, Gerdur Gröndal, Bjorn Gudbjornsson, Espen A. Haavardsholm, Kim Hørslev Petersen, Jon Lampa, Merete Lund Hetland, Dan Nordström, Mike Nurmohamed, Mikkel Østergaard, Anna Rudin, Marte Schrumpf Heiberg, Tuulikki Sokka‐Isler, Till Uhlig, Josien Veris‐van Dieren, Ronald van Vollenhoven, Tom Huizinga, Marcel JT Reinders, Sytske Anne Bergstra, Erik van den Akker, Rachel Knevel   +24 more
wiley   +1 more source

SACFNet: Spatial Attention and Channel Feature Fusion Network for Pulmonary Nodules Detection. [PDF]

J Imaging Inform Med
Zhang L, Jian M, Du J, Li X, Xu F, Yu H.
europepmc   +1 more source

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High Body Mass Index

Arthritis Care &Research, EarlyView.
Objective High body mass index (BMI ≥ 27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

Brain tumor segmentation using dual-stream multiscale 3D-UNET with dense net and spatial attention. [PDF]

Sci Rep
AlSekait DM, Zakariah M, Dubey P, Kashmeery T, Helal M.   +4 more
europepmc   +1 more source

The Association of Gout Flare Resolution With Physical Activity and Sleep: A Prospective Remote‐Monitoring Study

Arthritis Care &Research, EarlyView.
Objective This study aimed to determine whether gout flare resolution is associated with physical activity and sleep. These findings were intended to inform the future development of a definition for flare resolution. Methods Data from 81 participants with gout who experienced at least one flare during a six‐month prospective observational study were ...
Sarah Stewart, Boston Girven, Mia Clarke, Irene Zeng, Angelo Gaffo, Lisa K. Stamp, N. Lawrence Edwards, David Rice, Tania Ka'ai, Karl Rudolph, Julie Collis, Nicola Kayes, Nicola Dalbeth   +12 more
wiley   +1 more source

Covert spatial attention is uniform across cardinal meridians despite differential adaptation. [PDF]

J Vis
Lee HH, Carrasco M.
europepmc   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source