References of "Stern, Olivier"
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See detailPhenotype Classification of Zebrafish Embryos by Supervised Learning
Jeanray, Nathalie ULg; Marée, Raphaël ULg; Pruvot, Benoist ULg et al

Conference (2011, September 02)

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See detailZebrafish Skeleton Measurements using Image Analysis and Machine Learning Methods
Stern, Olivier ULg; Marée, Raphaël ULg; Aceto, Jessica ULg et al

Poster (2011, May 20)

The zebrafish is a model organism for biological studies on development and gene function. Our work aims at automating the detection of the cartilage skeleton and measuring several distances and angles to ... [more ▼]

The zebrafish is a model organism for biological studies on development and gene function. Our work aims at automating the detection of the cartilage skeleton and measuring several distances and angles to quantify its development following different experimental conditions. [less ▲]

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See detailAutomatic localization of interest points in zebrafish images with tree-based methods
Stern, Olivier ULg; Marée, Raphaël ULg; Aceto, Jessica ULg et al

in Proceedings of the 6th IAPR International Conference on Pattern Recognition in Bioinformatics (2011)

In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are ... [more ▼]

In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going through normal cell cycles, how organisms evolve in different experimental conditions, etc. But, with the large number of images acquired in high-throughput experiments, this manual inspection becomes lengthy, tedious and error-prone. In this paper, we propose to automatically detect specific interest points in microscopy images using machine learning methods with the aim of performing automatic morphometric measurements in the context of Zebrafish studies. We systematically evaluate variants of ensembles of classification and regression trees on four datasets corresponding to different imaging modalities and experimental conditions. Our results show that all variants are effective, with a slight advantage for multiple output methods, which are more robust to parameter choices. [less ▲]

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See detailZebrafish as model in toxicology/pharmacology.
Voncken, Audrey ULg; Piot, Amandine ULg; Stern, Olivier ULg et al

Poster (2010, March 17)

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See detailBiomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments
Marée, Raphaël ULg; Stern, Olivier ULg; Geurts, Pierre ULg

in CLEF Notebook Papers/LABs/Workshops (2010)

In this paper we describe our experiments related to the ImageCLEF 2010 medical modality classification task using extremely randomized trees. Our best run combines bags of textual and visual features. It ... [more ▼]

In this paper we describe our experiments related to the ImageCLEF 2010 medical modality classification task using extremely randomized trees. Our best run combines bags of textual and visual features. It yields 90% recognition rate and ranks 6th among 45 runs (ranging from 94% downto 12%). [less ▲]

Detailed reference viewed: 44 (8 ULg)