Overall Objectives
Research Program
Application Domains
- Sparse signal processing in chemistry
- Image restoration for two-photon microscopy
- Representation Learning for Biological Networks
- Breast tomosynthesis
- Inference of gene regulatory networks
- Imaging biomarkers and characterization for chronic lung diseases
- Imaging radiomics and genes to assess immunotherapy
- Development of a heart ventricle vessel generation model for perfusion analysis
New Software and Platforms
New Results
- General risk measures for robust machine learning
- Deep Latent Factor Model for Collaborative Filtering
- A Proximal Interior Point Algorithm with Applications to Image Processing
- Deep Unfolding of a Proximal Interior Point Method for Image Restoration
- Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images
- A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
- A probabilistic incremental proximal gradient method
- Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging
- Calibration-less parallel imaging compressed sensing reconstruction based on OSCAR regularization
- Proximal approaches for matrix optimization problems: Application to robust precision matrix estimation
- Representation Learning on Real-World Graphs
- Semi-supervised Learning for Misinformation Detection
- A Perturb and Combine Approach to Analyze Real-World Graphs
- Stochastic quasi-Fejér block-coordinate fixed point iterations with random sweeping: Mean-square and linear convergence
- Rational optimization for non- linear reconstruction with approximate penalization
- Deep neural network structures solving variational inequalities
- Generation of patient-specific cardiac vascular networks: a hybrid image-based and synthetic geometric model
- High throughput automated detection of axial malformations in Medaka fish embryo
- Quantitative PET in the context of lymphoma
- nD variational restoration of curvilinear structures with prior-based directional regularization
- Skin aging automated assessment
- Particle tracking
- Artificial Intelligence Applications for Thoracic imaging
- Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis
- U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets
- Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature
- A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks
- A multi-task deep learning framework coupling semantic segmentation and image reconstruction for very high resolution imagery
- Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
- Image Registration of Satellite Imagery with Deep Convolutional Neural Networks
- Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model Using Deep Non-Rigid Structure From Motion
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
Overall Objectives
Research Program
Application Domains
- Sparse signal processing in chemistry
- Image restoration for two-photon microscopy
- Representation Learning for Biological Networks
- Breast tomosynthesis
- Inference of gene regulatory networks
- Imaging biomarkers and characterization for chronic lung diseases
- Imaging radiomics and genes to assess immunotherapy
- Development of a heart ventricle vessel generation model for perfusion analysis
New Software and Platforms
New Results
- General risk measures for robust machine learning
- Deep Latent Factor Model for Collaborative Filtering
- A Proximal Interior Point Algorithm with Applications to Image Processing
- Deep Unfolding of a Proximal Interior Point Method for Image Restoration
- Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images
- A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
- A probabilistic incremental proximal gradient method
- Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging
- Calibration-less parallel imaging compressed sensing reconstruction based on OSCAR regularization
- Proximal approaches for matrix optimization problems: Application to robust precision matrix estimation
- Representation Learning on Real-World Graphs
- Semi-supervised Learning for Misinformation Detection
- A Perturb and Combine Approach to Analyze Real-World Graphs
- Stochastic quasi-Fejér block-coordinate fixed point iterations with random sweeping: Mean-square and linear convergence
- Rational optimization for non- linear reconstruction with approximate penalization
- Deep neural network structures solving variational inequalities
- Generation of patient-specific cardiac vascular networks: a hybrid image-based and synthetic geometric model
- High throughput automated detection of axial malformations in Medaka fish embryo
- Quantitative PET in the context of lymphoma
- nD variational restoration of curvilinear structures with prior-based directional regularization
- Skin aging automated assessment
- Particle tracking
- Artificial Intelligence Applications for Thoracic imaging
- Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis
- U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets
- Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature
- A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks
- A multi-task deep learning framework coupling semantic segmentation and image reconstruction for very high resolution imagery
- Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
- Image Registration of Satellite Imagery with Deep Convolutional Neural Networks
- Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model Using Deep Non-Rigid Structure From Motion
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
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