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Philips Research

Industry researcheurope · nl
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Research library23linked 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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EchoJEPA: A Latent Predictive Foundation Model for Echocardiography

Feb 02, 2026

This study addresses the challenge of disentangling anatomical structures from speckle noise and acquisition artifacts in echocardiography by introducing, for the first time in this domain, a latent prediction paradigm. Leveraging 18 million unlabeled images, a large-scale foundation model is developed to learn robust representations of cardiac anatomy through a latent prediction objective. The approach employs a frozen-backbone multi-view probing framework combined with a physics-informed acoustic perturbation evaluation strategy. Remarkably, using only 1% of labeled data, the model achieves 79% accuracy in view classification and improves estimation performance by approximately 20% for left ventricular ejection fraction and 17% for right ventricular systolic pressure. It demonstrates exceptional zero-shot generalization—particularly outperforming fine-tuned baselines on pediatric patients—and exhibits superior robustness to perturbations compared to existing methods.

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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.

0 citationsRead paper

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).

0 citationsRead paper

EchoJEPA: A Latent Predictive Foundation Model for Echocardiography

Feb 02, 2026

This study addresses the challenge of disentangling anatomical structures from speckle noise and acquisition artifacts in echocardiography by introducing, for the first time in this domain, a latent prediction paradigm. Leveraging 18 million unlabeled images, a large-scale foundation model is developed to learn robust representations of cardiac anatomy through a latent prediction objective. The approach employs a frozen-backbone multi-view probing framework combined with a physics-informed acoustic perturbation evaluation strategy. Remarkably, using only 1% of labeled data, the model achieves 79% accuracy in view classification and improves estimation performance by approximately 20% for left ventricular ejection fraction and 17% for right ventricular systolic pressure. It demonstrates exceptional zero-shot generalization—particularly outperforming fine-tuned baselines on pediatric patients—and exhibits superior robustness to perturbations compared to existing methods.

0 citationsRead paper