Ricardo Gamelas Sousa

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Computer aided diagnosis systems with the capability of automatically decide if a patient has or not a pathology and to hold the decision on the dificult cases, are becoming more frequent. The latter are afterwards reviewed by an expert reducing therefore time consuption on behalf of the expert. The number of cases to review depends on the cost of erring(More)
BACKGROUND Infection is a major complication after total joint arthroplasty. The urinary tract is a possible source of surgical site contamination, but the role of asymptomatic bacteriuria (ASB) before elective surgery and the subsequent risk of infection is poorly understood. METHODS Candidates for total hip or total knee arthroplasty were reviewed in a(More)
In this work we study the impact of a set of bag-offeatures strategies for the recognition of cancer in gastroenterology images. By using the SIFT descriptor, we analyzed the importance and performance impact of term weighting functions for the construction of visual vocabularies. Further analyzes were conducted in order to ascertain the robustness of(More)
Reject option is a technique used to improve classifier’s reliability in decision support systems. It consists in withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue has been(More)
Periprosthetic joint infection is a frequent complication after total hip replacement. Two-stage exchange with the use of a temporary cement spacer is commonplace. Several complications are possible with its use. In addition to infection persistence, mechanical complications such as dislocation or fractures are among the most common. Several risk factors(More)
Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal(More)
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order; however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data,(More)
In this work we consider the problem of binary classification where the classifier may abstain instead of classifying each observation, leaving the critical items for human evaluation. This article motivates and presents a novel method to learn the reject region on complex data. Observations are replicated and then a single binary classifier determines the(More)
Classification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item. In this paper we adapt a paradigm initially proposed for the classification of(More)