From representation learning to thematic classification - Application to hierarchical analysis of hyperspectral images

Numerous frameworks have been developed in order to analyze the increasing amount of available image data. Among those methods, supervised classification has received considerable attention leading to the development of state-of-the-art classification methods. These methods aim at inferring the class of each observation given a specific class nomenclature by exploiting a set of labeled observations. Thanks to extensive research efforts of the community, classification methods have become very efficient. Nevertheless, the results of a classification remains a highlevel interpretation of the scene since it only gives a single class to summarize all information in a given pixel. Contrary to classification methods, representation learning methods are model-based approaches designed especially to handle high-dimensional data and extract meaningful latent variables. By using physic-based models, these methods allow the user to extract very meaningful variables and get a very detailed interpretation of the considered image. The main objective of this thesis is to develop a unified framework for classification and representation learning. These two methods provide complementary approaches allowing to address the problem using a hierarchical modeling approach. The representation learning approach is used to build a low-level model of the data whereas classification is used to incorporate supervised information and may be seen as a high-level interpretation of the data. Two different paradigms, namely Bayesian models and optimization approaches, are explored to set up this hierarchical model. The proposed models are then tested in the specific context of hyperspectral imaging where the representation learning task is specified as a spectral unmixing proble

Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images

A large-scale feature selection wrapper is discussed for the classification of high dimensional remote sensing. An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and block matrix. The criterion function is split into two parts:one that is updated to test each feature and one that needs to be updated only once per feature selection. This split saved a lot of computation for each test. The algorithm is implemented in C++ and integrated into the Orfeo Toolbox. It has been compared to other classification algorithms on two high dimension remote sensing images. Results show that the approach provides good classification accuracies with low computation time

Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning

Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervised context. This paper proposes a unified framework to perform classification and low-level modeling jointly. The main objective is to use the estimated latent variables as features for classification and to incorporate simultaneously supervised information to help latent variable extraction. The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering these features into statistically homogeneous groups and a last classification stage exploiting the (possibly badly) labeled data. Performance of the model is assessed in the specific context of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and classification

Gaps at the interface between dentine and self‐adhesive resin cement in post‐endodontic restorations quantified in 3D by phase contrast‐enhanced micro‐CT

Aim: To assess the extent of gaps between root dentine and titanium or fibreglass post restorations following cementation with a self-adhesive resin cement. Methodology: Fourteen root filled maxillary central incisors restored with prefabricated posts made of Fibreglass (n = 7) or Titanium (n = 7) and cemented with RelyX Unicem 2 were imaged by rapid, high-resolution phase contrast-enhanced micro-CT (PCE-CT) in a synchrotron X-ray imaging facility (ID19, ESRF, 34 KeV, 0.65 µm pixel resolution). Reconstructions were used to measure canal, cement and post perimeters and cross-sectional areas and interfacial gaps at 0.1 mm increments in the root canal space, along the cervical region of the tooth. Remnants of endodontic sealer (AH Plus), when present, were also quantified. Mann–Whitney and 2-way ANOVA tests were used to compare findings within slices and between the two post groups. Pearson correlation coefficients (r) were determined between the interfacial gaps and the other measured parameters. Results: Clearly detectable gaps were found in 45% (±14%) of the interfaces between dentine and cement, along the canal in the cervical area of the tooth beneath the core. The length of interfacial gaps was moderately correlated to the canal cross-sectional area, to the canal perimeter and to the canal area filled by cement (R = 0.52 ~ 0.55, P 0.01). Both post types had defect-free interfaces with cement. Endodontic sealer remnants were found on ~10% of the canal walls and were moderately correlated to the presence of gaps. Approximately 30% of the sealer-affected interfaces exhibited no detachment between dentine, sealer and cement. Conclusions: Self-adhesive cements had interfacial gaps along substantial regions of the root canal surface, which was not correlated with the amount of cement in the canal. PCE-CT proved to be an excellent non-destructive method to study root canal restorations of hydrated samples in 3D

Modèle bayésien hiérarchique pour le démélange et la classification robuste d'images hyperspectrales

L’interprétation des images hyperspectrales demeure un problème complexe qui a été abordée sous différents paradigmes. En particulier, les techniques de classification supervisée et de démélange spectral sont deux familles de méthodes d’interprétation largement utilisées. Ces deux approches offrent des analyses complémentaires : le démélange spectral propose une modélisation basée sur une interprétation physique des images hyperspectrales, en supposant que chaque pixel est un mélange de spectres purs associés aux divers matériaux présents dans la scène, tandis que la classification supervisée cherche à identifier une classe unique par pixel en se basant sur un ensemble de classes sémantiques définies par l’utilisateur et sur un ensemble de données, labellisées par un expert, lui servant d’exemple. Si ces deux techniques ont été largement discutées dans la littérature, elles ont été rarement utilisées conjointement

Factorisation de matrices pour le démélange et la classification conjoints d'images hyperspectrales

La classification supervisée et le démélange spectral sont parmi les techniques les plus utilisées pour extraire l’information d’images hyperspectrales. Bien que ces deux méthodes sont couramment utilisées, elles n’ont que très rarement été envisagées conjointement. Au lieu d’utiliser ces méthodes de manière séquentielle, comme on le voit les travaux déjà réalisés [1], nous proposons ici d’introduire le concept de démélange et classification conjoints

Special cases : moons, rings, comets, trojans

Non-planetary bodies provide valuable insight into our current under- standing of planetary formation and evolution. Although these objects are challeng- ing to detect and characterize, the potential information to be drawn from them has motivated various searches through a number of techniques. Here, we briefly review the current status in the search of moons, rings, comets, and trojans in exoplanet systems and suggest what future discoveries may occur in the near future.Comment: Invited review (status August 2017

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Operational feature selection in gaussian mixture models

This report presents a forward feature selection algorithm based on Gaussian mixture model (GMM) classifiers. The algorithm selects iteratively features that maximize a criterion function which can be either a classification rate or a measure of divergence. We explore several variations of this algorithm by changing the criterion function and also by testing a floating forward variation allowing backward step to discard already selected features. An important effort is made in exploiting GMM properties to implement a fast algorithm. In particular, update rules of the GMM model are used to compute the criterion function with various sets of features. The result is a C++ remote module for the remote sensing processing toolbox Orfeo (OTB) developed by CNES. Finally, the method is tested and also compared to other classifiers using two different datasets, one of hyperspectral images with a lot of spectral variables and one with heterogeneous spatial features. The results validate the fact that the method performs well in terms of processing time and classification accuracy in comparison to the standard classifiers available in OTB