Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited nu...
In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RA...
Colorectal cancer (CRC) is the second popular cancer in females and third in males, with an increased number of cases. Pathology diagnoses complemented with predictive and prognostic biomarker information is the first step for personalized treatment....
Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We previously showed that augmenting error can enhance learning, but while such findings are encouraging, the methods need to be refined ...
This study uses machine learning (ML) to elucidate the contact relationship between the mandibular third molar (M3M) and the inferior alveolar canal (IAC), leading to three major contributions; (1) The first publicly accessible PR image dataset with ...
To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT) images, the study included 11,013 annotated LNs with a short-axis diameter ≥ 3 mm from 626 head an...
. Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to...
Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Feb 4, 2025
Generative deep learning has emerged as a promising data augmentation technique in recent years. This approach becomes particularly valuable in areas such as motion analysis, where it is challenging to collect substantial amounts of data. The objecti...
Time-series data such as fMRI and MEG carry a wealth of inherent spatio-temporal coupling relationship, and their modeling via deep learning is essential for uncovering biological mechanisms. However, current machine learning models for mining spatio...
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