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Philips Research

Industry researcheurope · nl
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Research library24linked papers
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Selected work

Representative Papers

Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction

Jul 04, 2026

This work addresses the degradation in image quality caused by k-space undersampling in unsupervised cine cardiac MRI reconstruction by proposing I-FP-INR, a novel image-domain dual-branch implicit neural representation framework. For the first time, an image feature embedding mechanism is integrated into implicit neural representations, enabling a backbone branch and a feature-processing branch to collaboratively learn complementary representations. This design enhances model expressiveness without requiring fully sampled reference data. By incorporating coil sensitivity encoding and an unsupervised learning strategy, I-FP-INR consistently outperforms existing baseline methods across multiple public and internal datasets, achieving superior reconstruction quality and demonstrating robust performance under varying sampling rates and diverse clinical scenarios.

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The Payment Heterogeneity Index: An Integrated Unsupervised Framework for High-Volume Procurement Oversight and Decision Support

May 09, 2026

This study addresses the lack of effective unsupervised monitoring tools for detecting anomalous behaviors in high-volume government procurement payments. The authors propose a Payment Heterogeneity Index (PHI) that integrates four dimensions—modality, asymmetry, tail behavior, and structural dispersion derived from a Gaussian Mixture Model (GMM)—thereby combining tail analysis with structural heterogeneity for the first time to uncover payment mechanism bifurcations overlooked by conventional metrics. Applied to municipal procurement data in the UK, PHI flagged 10.1% of suppliers as anomalous, exhibiting payment patterns markedly divergent from the norm; expert validation confirmed these cases warrant high investigative priority. Notably, PHI demonstrates distinct anomaly detection capability, showing low correlation with the coefficient of variation (ρ = 0.310).

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Recent publications

Latest Papers

Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction

Jul 04, 2026

This work addresses the degradation in image quality caused by k-space undersampling in unsupervised cine cardiac MRI reconstruction by proposing I-FP-INR, a novel image-domain dual-branch implicit neural representation framework. For the first time, an image feature embedding mechanism is integrated into implicit neural representations, enabling a backbone branch and a feature-processing branch to collaboratively learn complementary representations. This design enhances model expressiveness without requiring fully sampled reference data. By incorporating coil sensitivity encoding and an unsupervised learning strategy, I-FP-INR consistently outperforms existing baseline methods across multiple public and internal datasets, achieving superior reconstruction quality and demonstrating robust performance under varying sampling rates and diverse clinical scenarios.

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The Payment Heterogeneity Index: An Integrated Unsupervised Framework for High-Volume Procurement Oversight and Decision Support

May 09, 2026

This study addresses the lack of effective unsupervised monitoring tools for detecting anomalous behaviors in high-volume government procurement payments. The authors propose a Payment Heterogeneity Index (PHI) that integrates four dimensions—modality, asymmetry, tail behavior, and structural dispersion derived from a Gaussian Mixture Model (GMM)—thereby combining tail analysis with structural heterogeneity for the first time to uncover payment mechanism bifurcations overlooked by conventional metrics. Applied to municipal procurement data in the UK, PHI flagged 10.1% of suppliers as anomalous, exhibiting payment patterns markedly divergent from the norm; expert validation confirmed these cases warrant high investigative priority. Notably, PHI demonstrates distinct anomaly detection capability, showing low correlation with the coefficient of variation (ρ = 0.310).

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