BACKGROUND: In acute neck infections, magnetic resonance imaging (MRI) shows retropharyngeal edema (RPE), which is a prognostic imaging biomarker for a severe course of illness. This study aimed to develop a deep learning-based algorithm for the auto...
Few-shot learning alleviates the heavy dependence of medical image segmentation on large-scale labeled data, but it shows strong performance gaps when dealing with new tasks compared with traditional deep learning. Existing methods mainly learn the c...
Peripheral Nerves (PNs) are traditionally evaluated using US or MRI, allowing radiologists to identify and classify them as normal or pathological based on imaging findings, symptoms, and electrophysiological tests. However, the anatomical complexity...
This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality public...
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 16, 2025
This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and...
Cancer imaging : the official publication of the International Cancer Imaging Society
Jun 16, 2025
BACKGROUND: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast ...
Brain tumors, particularly glioblastoma multiforme, are considered one of the most threatening types of tumors in neuro-oncology. Segmenting brain tumors is a crucial part of medical imaging. It plays a key role in diagnosing conditions, planning tre...
OBJECTIVES: To evaluate a deep learning sequence-adaptive liver multiparametric MRI (mpMRI) assessment with validation in different populations using total and segmental T1 and T2 relaxation time maps.
BACKGROUND: Conventional magnetic resonance imaging (MRI) protocols for lower back pain require multiple sequences and long acquisition times, challenging healthcare systems amid rising demand for lumbar spine imaging.
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