Electroencephalography (EEG) preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE d...
BACKGROUND: T2-weighted imaging (T2WI), renowned for its sensitivity to edema and lesions, faces clinical limitations due to prolonged scanning time, increasing patient discomfort, and motion artifacts. The individual applications of artificial intel...
Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To addr...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 25, 2025
Cardiovascular implantable electronic devices (CIEDs) induce severe off-resonance artifacts in balanced steady-state free precession (bSSFP) cine MRI, limiting diagnostic utility for a growing patient population. While supervised and unpaired learnin...
Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminatin...
PURPOSE: To examine the effect of incorporating self-supervised denoising as a pre-processing step for training deep learning (DL) based reconstruction methods on data corrupted by Gaussian noise. K-space data employed for training are typically mult...
PURPOSE: Conventional quantitative susceptibility mapping (QSM) methods rely on simplified physical models that assume isotropic and homogeneous tissue properties, leading to artifacts and inaccuracies in biological tissues. This study aims to develo...
BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject...
We examine how the seemingly arbitrary way a prompt is posed, which we term "prompt architecture," influences responses provided by large language models (LLMs). Five large-scale, full-factorial experiments performing standard (zero-shot) similarity ...
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