Unsupervised Diverse Colorization via Generative Adversarial Networks

Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test of 80 humans further indicates our generated color schemes are highly convincible

Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping

The problem of defining appropriate distances between shapes or images and modeling the variability of natural images by group transformations is at the heart of modern image analysis. A current trend is the study of probabilistic and statistical aspects of deformation models, and the development of consistent statistical procedure for the estimation of template images. In this paper, we consider a set of images randomly warped from a mean template which has to be recovered. For this, we define an appropriate statistical parametric model to generate random diffeomorphic deformations in two-dimensions. Then, we focus on the problem of estimating the mean pattern when the images are observed with noise. This problem is challenging both from a theoretical and a practical point of view. M-estimation theory enables us to build an estimator defined as a minimizer of a well-tailored empirical criterion. We prove the convergence of this estimator and propose a gradient descent algorithm to compute this M-estimator in practice. Simulations of template extraction and an application to image clustering and classification are also provided

Kernel methods in medical imaging

We introduce machine learning techniques, more specifically kernel methods, and show how they can be used for medical imaging. After a tutorial presentation of machine learning concepts and tools, including Support Vector Machine (SVM), kernel ridge regression and kernel PCA, we present an application of these tools to the prediction of Computed Tomography (CT) images based on Magnetic Resonance (MR) images

Promoting oral potassium administration in a tertiary care hospital: An eleven-year study

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Quantitative prediction of cytochrome P450(CYP) 2D6-Mediated drug interactions

International audienceBackground and Objectives: An approach was recently proposed for quantitative predictions of cytochrome P450 (CYP) 3A4-mediated drug-drug interactions. This approach relies solely on in vivo data. It is based on two characteristic parameters: the contribution ratio (CR; i.e. the fraction of victim drug clearance due to metabolism by a specific CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when the inhibitor is co-administered and the AUC of the victim drug administered alone. The goals of our study were to extend this method to CYP2D6-mediated interactions, to validate it, and to forecast the magnitude of a large number of interactions that have not been studied so far.Methods: A three-step approach was pursued. First, initial estimates of CRs and IRs were obtained by several methods, using data from the literature. Second, an external validation of these initial estimates was carried out, by comparing the predicted AUC ratios with the observed values. Third, refined estimates of CRs and IRs were obtained by orthogonal regression in a Bayesian framework.Results: Thirty-nine AUC ratios were available for external validation. The mean prediction error of the ratios was 0.31, while the mean prediction absolute error was 1.14. Seventy AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 39 substrates and 11 inhibitors, respectively. The mean prediction error of the AUC ratios was 0.04, while the mean prediction absolute error was 0.51.Conclusions: Predictive distributions for 615 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far, such as metoclopramide, bupropion and terbinafine.</b

Generalized gradients: priors on minimization flows

This paper tackles an important aspect of the variational problem underlying active contours: optimization by gradient flows. Classically, the definition of a gradient depends directly on the choice of an inner product structure. This consideration is largely absent from the active contours literature. Most authors, explicitely or implicitely, assume that the space of admissible deformations is ruled by the canonical L 2 inner product. The classical gradient flows reported in the literature are relative to this particular choice. Here, we investigate the relevance of using (i) other inner products, yielding other gradient descents, and (ii) other minimizing flows not deriving from any inner product. In particular, we show how to induce different degrees of spatial consistency into the minimizing flow, in order to decrease the probability of getting trapped into irrelevant local minima. We report numerical experiments indicating that the sensitivity of the active contours method to initial conditions, which seriously limits its applicability and efficiency, is alleviated by our application-specific spatially coherent minimizing flows. We show that the choice of the inner product can be seen as a prior on the deformation fields and we present an extension of the definition of the gradient toward more general priors. 1