Saudi Arabic Sign Language (SArSL) recognition poses significant challenges due to its complex spatio-temporal structure and the scarcity of annotated datasets. This paper introduces a self-supervised learning framework built upon the Video Momentum ...
Classification methods based on deep learning require selecting between fully-supervised or weakly-supervised approaches, each presenting limitations in uncertainty quantification and interpretability. A framework unifying both supervision modes whil...
Supervised deep learning methods have been widely employed to detect floating macroplastic litter (>5 mm) in (fresh)water bodies. However, few studies used them to quantify floating litter fluxes in rivers with wide cross-sections, that is important ...
BACKGROUND: Dengue shock syndrome (DSS), with critical complications encompassing mechanical ventilation (MV), dengue-associated acute liver failure (PALF), and encephalitis, is associated with high mortality in children. Although serum lactate is a ...
Understanding the interplay between diseases and genes is crucial for gaining deeper insights into disease mechanisms and optimizing therapeutic strategies. In recent years, various computational methods have been developed to uncover potential disea...
The emergence of large foundation models (FMs) in histopathology, trained on extensive image datasets using high-performance graphics processing unit (GPU) clusters, has demonstrated significant potential in advancing computational pathology. FMs hav...
How can we build accurate transcription models for both ordinary speech and characterized speech in a semi-supervised setting? ASR (Automatic Speech Recognition) systems are widely used in various real-world applications, including translation system...
The implantation potential of an embryo is intricately linked to the quality of its blastocyst. Consequently, achieving an objective and precise identification of blastocyst morphology is imperative. The purpose of this study is to focus on the struc...
Mesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape ...
Diabetic retinopathy is a leading cause of vision loss, necessitating early, accurate detection. Automated deep learning models show promise but struggle with the complexity of retinal images and limited labeled data. Due to domain differences, tradi...
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