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In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polarimetric images by rescaling the contributions of channels in the network. In addition, we also design the adaptive polarization loss to make the network focus on the polarization information. Experiments show that our method can well restore the details flooded by serious noise and outperforms previous methods. Moreover, the underlying mechanism of channel attention is revealed visually.
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http://dx.doi.org/10.1364/OL.458514 | DOI Listing |
Brief Bioinform
September 2025
College of Computing and Data Science, Nanyang Technological University, 639798, Singapore.
Protein phosphorylation regulates protein function and cellular signaling pathways, and is strongly associated with diseases, including neurodegenerative disorders and cancer. Phosphorylation plays a critical role in regulating protein activity and cellular signaling by modulating protein-protein interactions (PPIs). It alters binding affinities and interaction networks, thereby influencing biological processes and maintaining cellular homeostasis.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Identifying the onset of the QRS complex is an important step for localizing the site of origin (SOO) of premature ventricular complexes (PVCs) and the exit site of Ventricular Tachycardia (VT). However, identifying the QRS onset is challenging due to signal noise, baseline wander, motion artifact, and muscle artifact. Furthermore, in VT, QRS onset detection is especially difficult due to the overlap with repolarization from the prior beat.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Heart Center, Department of Geriatrics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
The growing heterogeneity of cardiac patient data from hospitals and wearables necessitates predictive models that are tailored, comprehensible, and safeguard privacy. This study introduces PerFed-Cardio, a lightweight and interpretable semi-federated learning (Semi-FL) system for real-time cardiovascular risk stratification utilizing multimodal data, including cardiac imaging, physiological signals, and electronic health records (EHR). In contrast to conventional federated learning, where all clients engage uniformly, our methodology employs a personalized Semi-FL approach that enables high-capacity nodes (e.
View Article and Find Full Text PDFUnlabelled: Standard Automated Perimetry (SAP) is the mainstay for monitoring glaucoma progression and has been accepted by the U.S. Food and Drug Administration (FDA) as a trial endpoint, but only under stringent criteria of ≥7 dB loss in five pre-specified test locations.
View Article and Find Full Text PDFJ Cheminform
September 2025
College of Pharmacy, Chung-Ang University, Seoul, 06974, Republic of Korea.
The metabolic stability of a drug is a crucial determinant of its pharmacokinetic properties, including clearance, half-life, and oral bioavailability. Accurate predictions of metabolic stability can significantly streamline the drug discovery process. In this study, we present MetaboGNN, an advanced model for predicting liver metabolic stability based on Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL).
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