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Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.
Radiol Phys Technol
January 2025
Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures.
View Article and Find Full Text PDFPredictive analytics technique based on hybrid sampling to manage unbalanced data in smart cities.
Heliyon
December 2024
Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal, Madhya Pradesh, India.
A smart city is deemed smart enough because it has the capability to make decisions on its own. Artificial intelligence needs a lot of data from the physical world to make correct decisions. IoT sensor devices collect data from the surroundings, which is further used for predictive analytics.
View Article and Find Full Text PDFPRONAME: a user-friendly pipeline to process long-read nanopore metabarcoding data by generating high-quality consensus sequences.
Front Bioinform
December 2024
Bioengineering Unit, Life Sciences Department, Walloon Agricultural Research Centre, Gembloux, Belgium.
Background: The study of sample taxonomic composition has evolved from direct observations and labor-intensive morphological studies to different DNA sequencing methodologies. Most of these studies leverage the metabarcoding approach, which involves the amplification of a small taxonomically-informative portion of the genome and its subsequent high-throughput sequencing. Recent advances in sequencing technology brought by Oxford Nanopore Technologies have revolutionized the field, enabling portability, affordable cost and long-read sequencing, therefore leading to a significant increase in taxonomic resolution.
View Article and Find Full Text PDFPredicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.
Hum Brain Mapp
January 2025
Amsterdam UMC, Department of Radiology and Nuclear Medicine, University of Amsterdam, Amsterdam, the Netherlands.
Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.
View Article and Find Full Text PDFBLOOD PROTEOMICS AND MULTIMODAL RISK PROFILING of human volunteers after incision injury: a translational study for advancing Personalized Pain Management After Surgery.
Pharmacol Res
January 2025
Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, 44651 Muenster, Germany. Electronic address:
A significant number of patients develop chronic pain after surgery, but prediction of those who are at risk is currently not possible. Thus, prognostic prediction models that include bio-psycho, social and physiological factors in line with the complex nature of chronic pain would be urgently required. Here, we performed a translational study in male volunteers before an experimental incision injury.
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