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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.
Comput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
View Article and Find Full Text PDFMetAssimulo 2.0: a web app for simulating realistic 1D & 2D Metabolomic 1H NMR spectra.
Bioinformatics
January 2025
Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom.
Unlabelled: Metabolomics extensively utilizes Nuclear Magnetic Resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both one-dimensional (1D) and two-dimensional (2D) NMR spectra provide crucial information for metabolite annotation and quantification, yet present complex overlapping patterns which may require sophisticated machine learning algorithms to decipher. Unfortunately, the limited availability of labeled spectra can hamper application of machine learning, especially deep learning algorithms which require large amounts of labelled data.
View Article and Find Full Text PDFSuperior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study.
Radiol Med
January 2025
Medical Science Research Center, Korea University College of Medicine, Seoul, Republic of Korea.
Purpose: To compare the performance of ultrafast MRI with standard MRI in classifying histological factors and subtypes of invasive breast cancer among radiologists with varying experience.
Methods: From October 2021 to November 2022, this prospective study enrolled 225 participants with 233 breast cancers before treatment (NCT06104189 at clinicaltrials.gov).
New physiological insights using multi-TE ASL MRI measuring blood-brain barrier water exchange after caffeine intake.
MAGMA
January 2025
Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359, Bremen, Germany.
Objectives: Caffeine, a known neurostimulant and adenosine antagonist, affects brain physiology by decreasing cerebral blood flow. It interacts with adenosine receptors to induce vasoconstriction, potentially disrupting brain homeostasis. However, the impact of caffeine on blood-brain barrier (BBB) permeability to water remains underexplored.
View Article and Find Full Text PDFClinical and imaging characteristics of 135 cases of infectious sacroiliitis: a retrospective cohort study in China.
Clin Rheumatol
January 2025
Department of Rheumatology and Immunology, The First Medical Center, People Liberation Army General Hospital, Beijing, 100853, China.
To study the clinical, imaging, and computed tomography (CT)-guided biopsy pathology of patients with infectious sacroiliitis (ISI). We retrospectively analysed 135 patients diagnosed with ISI between 2008 and 2020, comprehensively evaluating clinical characteristics, laboratory test outcomes, pathological examination results, and magnetic resonance images (MRI). Among the 135 patients with ISI, 90 (66.
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