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In the last decades, the classification of images was established as a typical method for diagnosing many abnormalities and diseases. The purpose of an efficient classification method is considered essential in modern diagnostic medicine in order to increase the number of diagnosed patients and decrease the analysis time. The significant storage capabilities of electronic media have enabled research centers to accumulate repositories of classified (labeled) images and mostly of a large number of unclassified (unlabeled) images. Semi-supervised learning algorithms have become a hot topic of research as an alternative to traditional classification methods, seeing as they exploit the explicit classification information of labeled data with the knowledge hidden in the unlabeled data resulting in the creation of powerful and effective classifiers. In this work, we propose a new ensemble self-labeled algorithm, called DTCo, for X-ray classification. The efficacy of the presented algorithm is illustrated by a series of experiments against other state-of-the-art self-labeled methods.
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http://dx.doi.org/10.1007/978-3-030-32622-7_24 | DOI Listing |
Aim: To study the role of iodine, selenium and zinc in the pathogenesis of iodine deficiency and autoimmune thyroid diseases and scientifically substantiate the choice of security biomarkers and analytical methods for determination.
Materials And Methods: Serum iodine (I), selenium (Se), and zinc (Zn) concentrations were measured using inductively coupled plasma ionization tandem mass spectrometry (Agilent 8900 ICP-MS Triple Quad); by chemiluminescent immunoassay on an automatic analyzer Architect i2000 - TSH and AT-TPO in blood serum; by enzyme immunoassay - ZnT8A; biochemical method - ALP, SOD1 in 1150 people aged from 18 to 65 years (the average age of the subjects was 40±5 years). Ultrasound of the thyroid gland was performed in the supine position using a portable ultrasound machine LOGIQe with a multifrequency linear sensor 10-15 MHz; during the study, the volume of the thyroid gland, the presence of nodules and their characteristics according to the TIRADS classification, the structure of the thyroid gland and its echogenicity were assessed.
Sci Rep
March 2025
Division of Pain Medicine, Department of Anesthesiology, Roswell Park Comprehensive Cancer Center, Buffalo, 14263, USA.
Objective measurements of pain and safe methods to alleviate it could revolutionize medicine. This study used functional near-infrared spectroscopy (fNIRS) and virtual reality (VR) to improve pain assessment and explore non-pharmacological pain relief in cancer patients. Using resting-state fNIRS (rs-fNIRS) data and multinomial logistic regression (MLR), we identified brain-based pain biomarkers and classified pain severity in cancer patients.
View Article and Find Full Text PDFJ Gastrointest Cancer
March 2025
Houston Colon PLLC, Houston, TX, USA.
Low-grade cystic mucinous neoplasm of the sigmoid colon has never been previously reported and a classification for such a tumor does not currently exist. Here, we present a case of low-grade cystic mucinous neoplasm of the sigmoid colon and discuss the differential diagnosis especially as it relates to clinical management. Our case is a 68-year-old male who presented with anemia and a history of a lower gastrointestinal tract bleed.
View Article and Find Full Text PDFJ Mol Neurosci
March 2025
Department of Physics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science (SIMATS), Thandalam, Chennai, 602105, India.
Parkinson's disease recognition (PDR) involves identifying Parkinson's disease using clinical evaluations, imaging studies, and biomarkers, focusing on early symptoms like tremors, rigidity, and bradykinesia to facilitate timely treatment. However, due to noise, variability, and the non-stationary nature of EEG signals, distinguishing PD remains a challenge. Traditional deep learning methods struggle to capture the intricate temporal and spatial dependencies in EEG data, limiting their precision.
View Article and Find Full Text PDFAbdom Radiol (NY)
March 2025
Icahn School of Medicine Mount Sinai, BioMedical Engineering and Imaging Institute, New York, USA.
Purpose: Magnetic resonance elastography (MRE) measures liver stiffness for fibrosis staging, but its utility can be hindered by quality control (QC) challenges and measurement variability. The objective of the study was to fully automate liver MRE QC and liver stiffness measurement (LSM) using a deep learning (DL) method.
Methods: In this retrospective, single center, IRB-approved human study, a curated dataset involved 897 MRE magnitude slices from 146 2D MRE scans [1.
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