A Psychogenetic Algorithm for Behavioral Sequence Learning

This work presents an original algorithmic model of some essential features of psychogenetic theory, as was proposed by J.Piaget. Specifically, we modeled some elements of cognitive structure learning in children from 0 to 4 months of life. We are in fact convinced that the study of well-established cognitive models of human learning can suggest new, interesting approaches to problem so far not satisfactorily solved in the field of machine learning. Further, we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience. The model was implemented and tested in some simple experimental settings, with reference to the task of learning sensorimotor sequences

Testing the performance of image representations for mass classification in digital mammograms

In this paper a two-class classification problem is faced. One class is constituted by tumoral masses, breast tumors with size ranging from 3 mm to 30 mm. The other class is constituted by non-masses. A Support Vector Machine (SVM) is used as a classifier. Both, masses and non-masses, are extracted from the University of South Florida (USF) mammographic image database and are presented to the classifier as crops with pixel size 64 x 64. In order to find the optimal solution to this problem, different featureless crops representations are evaluated. In particular, a pixel-based representation, a Discrete Wavelet Transform (DWT) representation and an Overcomplete Wavelet Transform (OWT) representation are tested

Advanced Machine Learning Techniques for Digital Mammography

PDF and gzipped PostScript formats via anonymous FTP from the areaftp.cs.unibo.it:/pub/TR/UBLCS or via WWW a

VERY STRONGLY CONSTRAINED PROBLEMS: AN ANT COLONY OPTIMIZATION APPROACH

Abstract: Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This paper is composed of three parts

A ranklet-based CAD for digital mammography

Abstract. A novel approach to the detection of masses and clustered microcalcification is presented. Lesion detection is considered as a two-class pattern recognition problem. In order to get an effective and stable representation, the detection scheme codifies the image by using a ranklet transform. The vectors of ranklet coefficients obtained are classified by means of an SVM classifier. Our approach has two main advantages. First it does not need any feature selected by the trainer. Second, it is quite stable, with respect to the image histogram. That allows us to tune the detection parameters in one database and use the trained CAD on other databases without needing any adjustment. In this paper, training is accomplished on images coming from different databases (both digitized and digital). Test results are calculated on images coming from a few FFDM Giotto Image MD clinical units. The sensitivity of our CAD system is about 85 % with a false-positive rate of 0.5 marks per image. 1

A Novel Featureless Approach to Mass Detection in Digital Mammograms Based on Support Vector Machines

In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the masses appearance is the main obstacle of building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; on the contrary, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first SVM classifier. The detection task is here considered as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database