Computed Tomography Postprocessing for Abdominal Aortic Aneurysm Lumen Recognition in Unenhanced Examinations.

Introduction: Contrast medium (CM) use in computed tomography (CT) is limited by nephrotoxicity and possible allergic reactions. The purpose of this study is to introduce a tool for the diagnosis of abdominal aortic aneurysms (AAAs) by avoiding the use of CM.

Methods: With and without CM CTs of patients with AAA were evaluated.

Pointwise and uniform approximation by multivariate neural network operators of the max-product type.

In this article, the theory of multivariate max-product neural network (NN) and quasi-interpolation operators has been introduced. Pointwise and uniform approximation results have been proved, together with estimates concerning the rate of convergence. At the end, several examples of sigmoidal activation functions have been provided.

Neural network operators: Constructive interpolation of multivariate functions.

In this paper, the interpolation of multivariate data by operators of the neural network type is proved. These operators can also be used to approximate continuous functions defined on a box-domain of R(d). In order to show this fact, a uniform approximation theorem with order is proved.

Multivariate neural network operators with sigmoidal activation functions.

In this paper, we study pointwise and uniform convergence, as well as order of approximation, of a family of linear positive multivariate neural network (NN) operators with sigmoidal activation functions. The order of approximation is studied for functions belonging to suitable Lipschitz classes and using a moment-type approach. The special cases of NN operators, activated by logistic, hyperbolic tangent, and ramp sigmoidal functions are considered.

Approximation results for neural network operators activated by sigmoidal functions.

In this paper, we study pointwise and uniform convergence, as well as the order of approximation, for a family of linear positive neural network operators activated by certain sigmoidal functions. Only the case of functions of one variable is considered, but it can be expected that our results can be generalized to handle multivariate functions as well. Our approach allows us to extend previously existing results.