Showing papers by "Rafael Molina published in 1992"

Prognostic value of CA 19.9 levels in colorectal cancer.

Abstract: Pretreatment serum levels of carbohydrate antigen 19.9 (CA 19.9) and carcinoembryonic antigen were measured in 293 patients with colorectal cancer. Carbohydrate antigen 19.9 was above the cut-off limit of 37 U/mL in 35% of patients. Carbohydrate antigen 19.9 sensitivity was related to tumor stage. Carcinoembryonic antigen was above the cut-off level of 3.5 ng/mL in 61% of patients, and the simultaneous use of two markers increased sensitivity to 66%. The main use of pretreatment levels of CA 19.9 in locoregional cancer is in prognosis. Carbohydrate antigen 19.9 provided more prognostic information than that obtained by conventional staging methods. In patients with Dukes' C tumors, additional information was obtained for allocation of these patients into groups at low or high risk of recurrence. Prognostic significance of carcinoembryonic antigen was not independent of Dukes' classification.

A prospective study of tumor markers CA 125 and CA 19.9 in patients with epithelial ovarian carcinomas.

Abstract: In a prospective study, CA 125 and CA 19.9 serum levels were measured in 229 patients with ovarian cancer [121 with active disease, 108 in complete remission (CR)], and in 20 patients with other malig

Bayesian Deconvolution in Optical Astronomy

Abstract: Bayesian methods and spatial stochastic processes are used in the deconvolution of images of galaxies. Under very simple but realistic prior assumptions about the true underlying image of a galaxy the Bayesian framework is put to work. The method is tested in CCD images of extragalactic objects of different morphological types and an analysis of the deconvolutions obtained is performed emphazing the comparison with other observational results

On the Bayesian Approach to Learning

Abstract: The aim of this work is to introduce CASTLE (Causal Structures from Inductive Learning), a tool based on the bayesian approach to learning. CASTLE can be used so far to learn causal structures from raw data, propagate knowledge throughout polytrees, simulate and also edit polytree dependent distributions. CASTLE ([1] and [2]), is currently being developed by the authors in the DECSAI at the University of Granada. Basically, CASTLE estimates, from a file of examples, the (in)dependencies among the variables involved in the examples in order to build a polytree displaying such (in)dependencies. The steps to construct such polytree are: setting constrains among the variables, selecting a criterion to calculate the skeleton of the polytree and, finally, selecting the criterion to direct the obtained skeleton. Once the polytree is built, CASTLE allows the user to propagate knowledge throughout the obtained singly-connected graph using what has been called the bayesian approach to the knowledge propagation task. CASTLE can be also used as a platform where to test learning methods since it allows the users to create polytrees and simulate data from them and use the generated sample as learning samples.

Bayesian deconvolution with prior knowledge of object location : applications to ground-based planetary images

Abstract: A Bayesian method to deconvolve images when the location of the objects in the image is known in advance is presented. This knowledge of location is incorporated into the prior model via a labeling process. An iterative method is proposed to find the maximum a posteriori estimator of the image. The method was tested on both synthetic images and ground-based CCD images of Saturn with very encouraging results

On the Bayesian deconvolution of planets

Abstract: Considers Bayesian methods and spatial stochastic processes applied to the deconvolution of images of planets. Under simple but realistic prior assumptions about the true underlying image of a planet the Bayesian framework is put to work. The method has been tested on CCD images of Jupiter. >

Deconvolution in Optical Astronomy. A Bayesian Approach

Abstract: We describe in this work how the Bayesian paradigm can be applied to a deconvolution problem in optical astronomy. The use of robust statistics in this process is also discussed.

Non-supervised characterization of galaxies using different approaches

Abstract: In this paper we present some results for automatic characterization of galaxies from digital images. In a rst approach we try to characterize spirals and elliptical galaxies as realizations of MRF with diierent interaction parameters. In a second approach we attempt the characterization using some points on the galaxy contour that represent the shape of the galaxy, more precesily those with highest local curvature. Then we reduce the dimensionality of the problem using the boundary to characterize a galaxy shape.

Statistical Methods in Learning

Abstract: In this paper we describe an ESPRIT project known as ‘Stat-Log’ whose purpose is the comparison of learning algorithms. We give a brief summary of some of the algorithms in the project: linear and quadratic discriminant analysis, k nearest neighbour, CART, backpropagation, SMART, ALLOC80 and Pearl's polytree algorithm. We discuss the results obtained for two datasets, one of handwritten digits and the other of vehicle silhouettes.

Some Results on the Automatic Construction of Bayesian Networks

Abstract: When dealing with systems that contain a great quantity of knowledge, that one of the main problems we need to solve is to determine how much and what sort of knowledge is necessary to perform a given task (e.g. an inference or a diagnosis). In other words, it is essential to know what information is relevant to the question we are interested in.