Non-parametric Density Modeling and Outlier Detection in Medical Imaging Datasets

Abstract : The statistical analysis of medical images is challenging because of the high dimensionality and low signal-to-noise ratio of the data. Simple parametric statistical models, such as Gaussian distributions, are well-suited to high-dimensional settings. In practice, on medical data made of heterogeneous subjects, the Gaussian hypothesis seldom holds. In addition, alternative parametric models of the data tend to break down due to the presence of outliers that are usually removed manually from studies. Here we focus on interactive detection of these outlying observations, to guide the practitioner through the data inclusion process. Our contribution is to use Local Component Analysis as a non-parametric density estimator for this purpose. Experiments on real and simulated data show that our procedure separates well deviant observations from the relevant and representative ones. We show that it outperforms state-of-the-art approaches, in particular those involving a Gaussian assumption.
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Communication dans un congrès
Fei Wang and Dinggang Shen and Pingkun Yan and Kenji Suzuki. Machine Learning in Medical Imaging - Miccai 2012 workshop, Oct 2012, Nice, France. pp.207-214, 2012
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Contributeur : Virgile Fritsch <>
Soumis le : jeudi 4 octobre 2012 - 12:16:08
Dernière modification le : vendredi 22 juin 2018 - 01:20:43
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Virgile Fritsch, Gaël Varoquaux, Jean-Baptiste Poline, Bertrand Thirion. Non-parametric Density Modeling and Outlier Detection in Medical Imaging Datasets. Fei Wang and Dinggang Shen and Pingkun Yan and Kenji Suzuki. Machine Learning in Medical Imaging - Miccai 2012 workshop, Oct 2012, Nice, France. pp.207-214, 2012. 〈hal-00738438〉

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