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Unsupervised Neural Decoding to Predict Dexterous Multi-Finger Flexion and Extension Forces.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Accurate control over individual fingers of robotic hands is essential for the progression of human-robot interactions. Accurate prediction of finger forces becomes imperative in this context. The state-of-the-art neural decoders can extract neural s...
Syn-Net: A Synchronous Frequency-Perception Fusion Network for Breast Tumor Segmentation in Ultrasound Images.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Accurate breast tumor segmentation in ultrasound images is a crucial step in medical diagnosis and locating the tumor region. However, segmentation faces numerous challenges due to the complexity of ultrasound images, similar intensity distributions,...
AdaptEEG: A Deep Subdomain Adaptation Network With Class Confusion Loss for Cross-Subject Mental Workload Classification.
IEEE journal of biomedical and health informatics
Mar 6, 2025
EEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation...
Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of ...
Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 6, 2025
Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., beca...
Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.
Emerging microbes & infections
Mar 5, 2025
To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoencephalitis (STME) and to construct and validate predictive models using machine learning.Clinical data were collected from 100 cases of pediatric STM...
Model updating strategy study about sex identification of silkworm pupae using transfer learning and NIR spectroscopy.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Mar 5, 2025
This paper proposes a novel model updating strategy named SilkwormNet for the first time to address the sex discrimination problem of silkworm pupae with new species. SilkwormNet integrates a ResNet block, a multi-head attention mechanism, and a Sche...
Involvement of disulfidptosis in the pathophysiology of autism spectrum disorder.
Life sciences
Mar 5, 2025
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, with oxidative stress recognized as a key pathogenic mechanisms. Oxidative stress disrupts intracellular dynamic- thiol/disulfide homeostasis (DTDH), potentially leading to disu...
Deep learning based estimation of heart surface potentials.
Artificial intelligence in medicine
Mar 5, 2025
Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting from body surface potentials. This is classically based on geometric information on the torso and the heart from imaging, which complicates clinical ...
Utilizing Machine Learning Techniques to Predict Negative Remodeling in Uncomplicated Type B Intramural Hematoma.
Annals of vascular surgery
Mar 5, 2025
BACKGROUND: To evaluate the effectiveness of machine learning (ML) techniques in predicting negative remodeling in uncomplicated Stanford type B intramural hematoma (IMHB) during the acute phase.
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