Covert visual attention decoding from EEG signals is a key challenge in cognitive neuroscience and brain-computer interface applications. Traditional approaches often rely on manual feature extraction and handcrafted pipelines, which limit scalabilit...
The clinical challenges in monitoring high-incidence complications in patients with colostomy after colorectal cancer surgery have led to the development of an intelligent monitoring system based on deep learning and augmented reality technology in t...
Given South Korea's recent 16.6% reduction in research and development (R&D) budgets for 2023, there is an urgent need for more efficient and strategic R&D policy management. Previous studies evaluating R&D outputs have primarily relied on quantitati...
The growth and productivity of banana crops are critically affected by micronutrient deficiencies, which are often difficult to detect at early stages. Lightweight deep learning models, optimized through neural architecture search (NAS) and attention...
This work describes a publicly available dataset, the Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg), consisting of 1,255 cervical spine magnetic resonance imaging (MRI) examinations from 1,232 patients collected from the Duke Un...
Tuberculosis (TB) remains a major global health burden, particularly in low-resource, high-prevalence regions. Pediatric TB diagnosis poses challenges with non-specific symptoms and less distinct radiological manifestations than adult TB. Many affect...
PURPOSE: Lateral lymph node dissection for rectal cancer is challenging because of the presence of blood vessels and nerves essential for postoperative genitourinary function and leg movements. Identifying these structures during surgery is crucial. ...
Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly important for identifying epileptogenic zones in patients with drug-resistant epilepsy. However, few open-...
Respiratory organoids have emerged as a powerful in vitro model for studying respiratory diseases and drug discovery. However, the high-throughput analysis of organoid images remains a challenge due to the lack of automated and accurate segmentation ...
PURPOSE: A deep learning model integrating CT radiomics and clinical features was developed to predict perioperative complications and risk grade in patients undergoing partial nephrectomy, and was compared to traditional anatomical classification mo...
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