OPIS - 2019 - Annual activity report

OPIS

OPIS - 2019

Project-Team Opis

Team, Visitors, External Collaborators

Overall Objectives

OPIS

Research Program

Application Domains

Highlights of the Year

New Software and Platforms

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 $ℓ_{0}$ 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

Bilateral Contracts with Industry

Partnerships and Cooperations

Dissemination

Bibliography

Publications of the year

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Section: Application Domains

Breast tomosynthesis

Participants: Emilie Chouzenoux, Jean-Christophe Pesquet, Maissa Sghaier (collaboration G. Palma, GE Healthcare)

Breast cancer is the most frequently diagnosed cancer for women. Mammography is the most used imagery tool for detecting and diagnosing this type of cancer. Since it consists of a 2D projection method, this technique is sensitive to geometrical limitations such as the superimposition of tissues which may reduce the visibility of lesions or make even appear false structures which are interpreted by radiologists as suspicious signs. Digital breast tomosynthesis allows these limitations to be circumvented. This technique is grounded on the acquisition of a set of projections with a limited angle view. Then, a 3D estimation of the sensed object is performed from this set of projections, so reducing the overlap of structures and improving the visibility and detectability of lesions possibly present in the breast. The objective of our work is to develop a high quality reconstruction methodology where the full pipeline of data processing is modeled [50].

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