Results 1-20 of 162.
pierre geurts

Bookmark and Share    
Full Text
Peer Reviewed
See detailAutomated multimodal volume registration based on supervised 3D anatomical landmark detection
Vandaele, Rémy ULiege; LALLEMAND, François ULiege; MARTINIVE, Philippe ULiege et al

in SCITEPRESS Digital Library (in press)

We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely ... [more ▼]

We propose a new method for automatic 3D multimodal registration based on anatomical landmark detection. Landmark detectors are learned independantly in the two imaging modalities using Extremely Randomized Trees and multi-resolution voxel windows. A least-squares fitting algorithm is then used for rigid registration based on the landmark positions as predicted by these detectors in the two imaging modalities. Experiments are carried out with this method on a dataset of pelvis CT and CBCT scans related to 45 patients. On this dataset, our fully automatic approach yields results very competitive with respect to a manually assisted state-of-the-art rigid registration algorithm. [less ▲]

Detailed reference viewed: 167 (33 ULiège)
Full Text
Peer Reviewed
See detailSepsis prediction in critically ill patients by platelet activation markers on ICU admission: a prospective pilot study
LAYIOS, Nathalie ULiege; Delierneux, Céline ULiege; Hego, Alexandre ULiege et al

in Intensive Care Medicine Experimental (2017), 5(1), 32

Background: Platelets have been involved in both surveillance and host defense against severe infection. To date, whether platelet phenotype or other hemostasis components could be associated with ... [more ▼]

Background: Platelets have been involved in both surveillance and host defense against severe infection. To date, whether platelet phenotype or other hemostasis components could be associated with predisposition to sepsis in critical illness remains unknown. The aim of this work was to identify platelet markers that could predict sepsis occurrence in critically ill injured patients. Results: This single-center, prospective, observational, 7-month study was based on a cohort of 99 non-infected adult patients admitted to ICUs for elective cardiac surgery, trauma, acute brain injury and post-operative prolonged ventilation and followed up during ICU stay. Clinical characteristics and severity score (SOFA) were recorded on admission. Platelet activation markers, including fibrinogen binding to platelets, platelet membrane P-selectin expression, plasma soluble CD40L, and platelet-leukocytes aggregates were assayed by flow cytometry at admission and 48h later, and also at the time of sepsis diagnosis (Sepsis-3 criteria) and 7 days later for sepsis patients. Hospitalization data and outcomes were also recorded. Of the 99 patients, 19 developed sepsis after a median time of 5 days. SOFA at admission was higher; their levels of fibrinogen binding to platelets (platelet-Fg) and of D-dimers were significantly increased compared to the other patients. Levels 48h after ICU admission were no longer significant. Platelet-Fg % was an independent predictor of sepsis (P = 0.030). By ROC curve analysis cutoff points for SOFA (AUC=0.85) and Platelet-Fg (AUC=0.75) were 8 and 50%, respectively. The prior risk of sepsis (19%) increased to 50% when SOFA was above 8, to 46% when Platelet-Fg was above 50%, and to 87% when both SOFA and Platelet-Fg were above their cutoff values. By contrast, when the two parameters were below their cutoffs, the risk of sepsis was negligible (3.8%). Patients with sepsis had longer ICU and hospital stays and higher death rate. Conclusion: In addition to SOFA, platelet-bound fibrinogen levels assayed by flow cytometry within 24h of ICU admission help identifying critically ill patients at risk of developing sepsis. [less ▲]

Detailed reference viewed: 57 (14 ULiège)
Full Text
Peer Reviewed
See detailSCENIC: single-cell regulatory network inference and clustering
Aibar, Sara; González-Blas, Carmen Bravo; Moerman, Thomas et al

in Nature Methods (2017), 14

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium ... [more ▼]

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity. [less ▲]

Detailed reference viewed: 16 (4 ULiège)
Full Text
Peer Reviewed
See detailA two-step methodology for human pose estimation increasing the accuracy and reducing the amount of learning samples dramatically
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Geurts, Pierre ULiege et al

in Advanced Concepts for Intelligent Vision Systems (2017, September)

In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we ... [more ▼]

In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we first estimate the orientation of the standing person seen by the camera and then use this information to dynamically select a pose estimation model suited for this particular orientation. We evaluated our method on a public dataset of realistic depth images with precise ground truth joints location. Our experiments show that our method decreases the error of a state-of-the-art pose estimation method by 30%, or reduces the size of the needed learning set by a factor larger than 10. [less ▲]

Detailed reference viewed: 49 (6 ULiège)
Full Text
Peer Reviewed
See detailTree Ensemble Methods and Parcelling to Identify Brain Areas Related to Alzheimer’s Disease
Wehenkel, Marie ULiege; Bastin, Christine ULiege; Phillips, Christophe ULiege et al

in 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), proceedings (2017, June)

Detailed reference viewed: 36 (9 ULiège)
Full Text
Peer Reviewed
See detailSimple connectome inference from partial correlation statistics in calcium imaging
Sutera, Antonio ULiege; Joly, Arnaud ULiege; François-Lavet, Vincent et al

in Soriano, Jordi; Battaglia, Demian; Guyon, Isabelle (Eds.) et al Neural Connectomics Challenge (2017)

In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to ... [more ▼]

In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation statistics. This paper summarises the methodology that led us to win the Connectomics Challenge, proposes a simplified version of our method, and finally compares our results with respect to other inference methods. [less ▲]

Detailed reference viewed: 145 (10 ULiège)
Full Text
See detailExploiting random projections and sparsity with random forests and gradient boosting methods - Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity
Joly, Arnaud ULiege

Doctoral thesis (2017)

Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output ... [more ▼]

Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output relationship through a series of nested ``if-then-else'' questions, the testing nodes, leading to a set of predictions, the leaf nodes. Several of such trees are often combined together for state-of-the-art performance: random forest ensembles average the predictions of randomized decision trees trained independently in parallel, while tree boosting ensembles train decision trees sequentially to refine the predictions made by the previous ones. The emergence of new applications requires scalable supervised learning algorithms in terms of computational power and memory space with respect to the number of inputs, outputs, and observations without sacrificing accuracy. In this thesis, we identify three main areas where decision tree methods could be improved for which we provide and evaluate original algorithmic solutions: (i) learning over high dimensional output spaces, (ii) learning with large sample datasets and stringent memory constraints at prediction time and (iii) learning over high dimensional sparse input spaces. A first approach to solve learning tasks with a high dimensional output space, called binary relevance or single target, is to train one decision tree ensemble per output. However, it completely neglects the potential correlations existing between the outputs. An alternative approach called multi-output decision trees fits a single decision tree ensemble targeting simultaneously all the outputs, assuming that all outputs are correlated. Nevertheless, both approaches have (i) exactly the same computational complexity and (ii) target extreme output correlation structures. In our first contribution, we show how to combine random projection of the output space, a dimensionality reduction method, with the random forest algorithm decreasing the learning time complexity. The accuracy is preserved, and may even be improved by reaching a different bias-variance tradeoff. In our second contribution, we first formally adapt the gradient boosting ensemble method to multi-output supervised learning tasks such as multi-output regression and multi-label classification. We then propose to combine single random projections of the output space with gradient boosting on such tasks to adapt automatically to the output correlation structure. The random forest algorithm often generates large ensembles of complex models thanks to the availability of a large number of observations. However, the space complexity of such models, proportional to their total number of nodes, is often prohibitive, and therefore these modes are not well suited under stringent memory constraints at prediction time. In our third contribution, we propose to compress these ensembles by solving a L1-based regularization problem over the set of indicator functions defined by all their nodes. Some supervised learning tasks have a high dimensional but sparse input space, where each observation has only a few of the input variables that have non zero values. Standard decision tree implementations are not well adapted to treat sparse input spaces, unlike other supervised learning techniques such as support vector machines or linear models. In our fourth contribution, we show how to exploit algorithmically the input space sparsity within decision tree methods. Our implementation yields a significant speed up both on synthetic and real datasets, while leading to exactly the same model. It also reduces the required memory to grow such models by exploiting sparse instead of dense memory storage for the input matrix. [less ▲]

Detailed reference viewed: 202 (22 ULiège)
Full Text
Peer Reviewed
See detailGlobally Induced Forest: A Prepruning Compression Scheme
Begon, Jean-Michel ULiege; Joly, Arnaud; Geurts, Pierre ULiege

in Proceedings of Machine Learning Research (2017), 70

Tree-based ensemble models are heavy memory- wise. An undesired state of affairs consider- ing nowadays datasets, memory-constrained environment and fitting/prediction times. In this paper, we propose the ... [more ▼]

Tree-based ensemble models are heavy memory- wise. An undesired state of affairs consider- ing nowadays datasets, memory-constrained environment and fitting/prediction times. In this paper, we propose the Globally Induced Forest (GIF) to remedy this problem. GIF is a fast prepruning approach to build lightweight ensembles by iteratively deepening the current forest. It mixes local and global optimizations to produce accurate predictions under memory constraints in reasonable time. We show that the proposed method is more than competitive with standard tree-based ensembles under corresponding constraints, and can sometimes even surpass much larger models. [less ▲]

Detailed reference viewed: 61 (2 ULiège)
Peer Reviewed
See detailA membrane-type- matrix metalloproteinase (MT1-MMP) - discoidin domain receptor 1 axis regulates collagen-induced apoptosis in breast cancer cells
Assent, Delphine; Bourgot, Isabelle ULiege; Hennuy, Benoit et al

Poster (2016, October)

During tumour dissemination, invading breast carcinoma cells become confronted with a reactive stroma, a type I collagen-rich environment endowed with anti-proliferative and proapoptotic properties. To ... [more ▼]

During tumour dissemination, invading breast carcinoma cells become confronted with a reactive stroma, a type I collagen-rich environment endowed with anti-proliferative and proapoptotic properties. To develop metastatic capabilities, tumour cells must acquire the capacity to cope with this novel microenvironment. How cells interact with and respond to their microenvironment during cancer dissemination remains poorly understood. To address the impact of type I collagen on the fate of tumour cells, human breast carcinoma MCF-7 cells were cultured within three-dimensional type I collagen gels (3D COL1). Using this experimental model, we demonstrate that membrane type-1 matrix metalloproteinase (MT1-MMP), a proteinase overexpressed in many aggressive tumours, promotes tumour progression by circumventing the collagen-induced up-regulation of BIK, a pro-apoptotic tumour suppressor, and hence apoptosis. A transcriptomic analysis was performed to decipher the molecular mechanisms regulating 3D COL1-induced apoptosis in human breast cancer cells. Control and MT1-MMP expressing MCF-7 cells were cultured on two-dimensional plastic plates or within 3D COL1 and a global transcriptional time-course analysis was performed. Shifting the cells from plastic plates to 3D COL1 activated a complex reprogramming of genes implicated in various biological processes. Bioinformatic analysis revealed a 3D COL1-mediated alteration of key cellular functions including apoptosis, cell proliferation, RNA processing and cytoskeleton remodelling. By using a panel of pharmacological inhibitors, we identified discoidin domain receptor 1 (DDR1), a receptor tyrosine kinase specifically activated by collagen, as the initiator of 3D COL1-induced apoptosis. Our data support the concept that MT1-MMP contributes to the inactivation of the DDR1-BIK signalling axis through the cleavage of collagen fibres and/or the alteration of DDR1 receptor signalling unit, without triggering drastic alterations of the transcriptome of MCF-7 cells. [less ▲]

Detailed reference viewed: 37 (6 ULiège)
Full Text
Peer Reviewed
See detailRandom subspace with trees for feature selection under memory constraints
Sutera, Antonio ULiege; Châtel, Célia; Louppe, Gilles et al

Conference (2016, September 12)

Detailed reference viewed: 251 (18 ULiège)
Full Text
Peer Reviewed
See detailJoint learning and pruning of decision forests
Begon, Jean-Michel ULiege; Joly, Arnaud ULiege; Geurts, Pierre ULiege

Conference (2016, September 12)

Decision forests such as Random Forests and Extremely randomized trees are state-of-the-art supervised learning methods. Unfortunately, they tend to consume much memory space. In this work, we propose an ... [more ▼]

Decision forests such as Random Forests and Extremely randomized trees are state-of-the-art supervised learning methods. Unfortunately, they tend to consume much memory space. In this work, we propose an alternative algorithm to derive decision forests under heavy memory constraints. We show that under such constraints our method usually outperforms simpler baselines and can even sometimes beat the original forest. [less ▲]

Detailed reference viewed: 71 (11 ULiège)
Full Text
Peer Reviewed
See detailContext-dependent feature analysis with random forests
Sutera, Antonio ULiege; Louppe, Gilles; Huynh-Thu, Vân Anh ULiege et al

in Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016) (2016, June)

Detailed reference viewed: 152 (29 ULiège)
Full Text
See detailComments on: A random forest guided tour
Geurts, Pierre ULiege; Wehenkel, Louis ULiege

in TEST (2016), 25(2), 247-253

Detailed reference viewed: 30 (1 ULiège)
Full Text
Peer Reviewed
See detailCollaborative analysis of multi-gigapixel imaging data using Cytomine
Marée, Raphaël ULiege; Rollus, Loïc; Stévens, Benjamin et al

in Bioinformatics (2016)

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of ... [more ▼]

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share, and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications. Availability: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/. A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available. [less ▲]

Detailed reference viewed: 183 (32 ULiège)
Full Text
Peer Reviewed
See detailTowards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Wehenkel, Louis ULiege

in Pattern Recognition Letters (2016)

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are ... [more ▼]

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are the extraction of random subwindows described by raw pixel intensity values and the use of ensemble of extremely randomized trees to directly classify images or to learn image features. The influence of method parameters and variants is thoroughly evaluated so as to provide baselines and guidelines for future studies. Detailed results are provided on 80 publicly available datasets that depict very diverse types of images (more than 3800 image classes and over 1.5 million images). [less ▲]

Detailed reference viewed: 487 (64 ULiège)
Peer Reviewed
See detailCirculating microRNA-based screening tool for breast cancer
Freres, Pierre ULiege; Wenric, Stéphane ULiege; Boukerroucha, Meriem et al

in Oncotarget (2015)

Circulating microRNAs (miRNAs) are increasingly recognized as powerful biomarkers in several pathologies, including breast cancer. Here, their plasmatic levels were measured to be used as an alternative ... [more ▼]

Circulating microRNAs (miRNAs) are increasingly recognized as powerful biomarkers in several pathologies, including breast cancer. Here, their plasmatic levels were measured to be used as an alternative screening procedure to mammography for breast cancer diagnosis. A plasma miRNA profile was determined by RT-qPCR in a cohort of 378 women. A diagnostic model was designed based on the expression of 8 miRNAs measured first in a profiling cohort composed of 41 primary breast cancers and 45 controls, and further validated in diverse cohorts composed of 108 primary breast cancers, 88 controls, 35 breast cancers in remission, 31 metastatic breast cancers and 30 gynecologic tumors. A receiver operating characteristic curve derived from the 8-miRNA random forest based diagnostic tool exhibited an area under the curve of 0.81. The accuracy of the diagnostic tool remained unchanged considering age and tumor stage. The miRNA signature correctly identified patients with metastatic breast cancer. The use of the classification model on cohorts of patients with breast cancers in remission and with gynecologic cancers yielded prediction distributions similar to that of the control group. Using a multivariate supervised learning method and a set of 8 circulating miRNAs, we designed an accurate, minimally invasive screening tool for breast cancer. [less ▲]

Detailed reference viewed: 100 (32 ULiège)