1,721,038 research outputs found
An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules
No full textTwo class classifiers are used in many complex problems in which the classification results could have serious consequences. In such situations the cost for a wrong classification can be so high that can be convenient to avoid a decision and reject the sample. This paper presents a comparison between two different reject rules (the Chow’s and the ROC rule). In particular, the experiments show that the Chow’s rule is inappropriate when the estimates of the a posteriori probabilities are not reliable
An effective learning strategy for cascaded object detection
Full text linkTo distinguish objects from non-objects in images under computational constraints, a suitable solution is to employ a cascade detector that consists of a sequence of node classifiers with increasing discriminative power. However, among the millions of image patches generated from an input image, only very few contain the searched object. When trained on these highly unbalanced data sets, the node classifiers tend to have poor performance on the minority class. Thus, we propose a learning strategy aimed at maximizing the node classi-fiers ranking capability rather than their accuracy. We also provide an efficient implementation yielding the same time complexity of the original Viola-Jones cascade training. Experimental results on highly unbalanced real problems show that our approach is both efficient and effective when compared to other node training strategies for skewed classes
Multi agent systems for circuit tolerance and sensitivity analysis
No full textIn this work we investigate the applicability of the multi agent paradigm to the realization of a distributed software system for circuit Tolerance and Sensitivity Analysis (TSA). A Multi Agent System (MAS) is specifically structured to handle the interactions among several dedicated software tools, each designed for the application of a given method of TSA. The typical application of the MAS proposed in this paper is in the field of circuit TSA. The examples presented confirm the potentiality of the MAS approach in numerical computations as well as in testing and evaluation of methods of analysis
A Ranking-based Cascade Approach for Unbalanced Data
No full textIn this paper we present a cascade-based framework
for object detection in which the node classifiers are
trained by a learning algorithm based on ranking in-
stead of classification error. Such an approach is par-
ticularly suited for facing the asymmetry between pos-
itive and negative class, that is a huge problem in ob-
ject detection applications. Other methods focused on
this problem and previously proposed, such as Asym-
Boost, rely on an asymmetric weight updating mech-
anism of the samples based on a parameter k which
estimates the degree of skewing between the classes.
Actually such parameter is difficult to choose and re-
quires a significant tuning activity during the training
phase. On the contrary, our approach is nonparametric
and has demonstrated to provide slightly better perfor-
mance when compared with AsymBoost on a real detec-
tion problem
Transfer learning in breast mass detection and classification
No full textCovid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets
Detecting Clusters of Microcalcifications with a Cascade-Based Approach
No full textIn this paper we present a cascade-based framework to detect clusters of microcalcifications on mammograms. The algorithm is based on a sliding window technique where a detector is structured as a “cascade” of simple boosting classifiers with increasing complexity. Such a method couples the effectiveness of the cascade approach with the Rank-
Boost algorithm that is aimed at maximizing the area under the ROC curve and represents a good choice when dealing with unbalanced data sets
Cascaded Rank-Based Classifiers for Detecting Clusters of Microcalcifications
No full textA Computer Aided Detection (CAD) system has frequently to deal with a significant skew between positive and negative class. For this reason we propose a solution based on an ensemble of classifiers structured as a “cascade” of dichotomizers where each node is robust to such skew since it is trained by a learning algorithm based on ranking instead of classification error. The proposed approach has been applied to the detection of clusters of microcalcifications in mammograms and has shown good performance in comparison with other methods well suited to deal with unbalanced problems
A Boosting-Based Approach to Refine the Segmentation of Masses in Mammography
No full textIn this paper we present an algorithm for finding an accurate estimate of the contour of masses in mammograms. We assume that a rough estimate of the region containing the mass is known: in particular it is available the location of an area inside the mass (core) and a closed curve beyond which the mass does not extend. The proposed method employs a boosting-based classifier trained on the core and on a background region beyond the external contour, so that it provides an accurate estimate of the mass contour by classifying unlabeled pixels between the core and the external contour. The proposed approach is useful not only for automatic localization of mass contour, but also as a powerful tool during annotation of mammograms, given that an user provides interactively an estimate for the core and the external contour of the mass. The approach has been verified on a set of mammograms showing very encouraging results
Personalised Intelligent Training on the Web: A Multi Agent Approach
No full textOne of the most interesting realm among those ones brought up to success by the development of the Internet is distance learning and training. For this reason. the investigation for adeguate architectures and platforms supporting flexible and tailored training solutions is nowadays 01' great interests in the
scientific community. This paper is concerned with the presentation of an original architecture for intelligent distance tutoring which make use of
software agents. The way in which the knowledge is represented and stored is discussed together with the ability of our system to manage individual learning paths far different users. The rationale far using Agents is presented
and the implementation of the system is discussed
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