Inês Domingues

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RATIONALE AND OBJECTIVES Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and(More)
CONTEXT Mammography is the most effective procedure for an early detection of the breast abnormalities. Masses are a type of abnormality, which are very difficult to be visually detected on mammograms. AIMS In this paper an efficient method for detection of masses in mammograms is implemented. SETTINGS AND DESIGN The proposed mass detector consists of(More)
Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. The study of this disease using computer science tools resorts often to the image segmentation operation. Image segmentation, although having been extensively studied, is still an(More)
The automatic detection and segmentation of the pectoral muscle in the medio-lateral oblique view of mammograms is essential for further analysis of breast anormalies. However, it is still a very difficult task since the sizes, shapes and intensity contrasts of pectoral muscles change greatly from image to image. In this paper, an algorithm based on the(More)
Automatic pectoral muscle removal on medio-lateral oblique view of mammogram is an essential step for many mammographic processing algorithms. However, the wide variability in the position of the muscle contour, together with the similarity between in muscle and breast tissues makes the detection a difficult task. In this paper, we propose a two step(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 the predictive modeling tasks, a clear distinction is often made between learning problems that are supervised or unsupervised, the first involving only labeled data (training patterns with known category labels) while the latter involving only unlabeled data. There is a growing interest in a hybrid setting, called semi-supervised learning, in(More)
Deep Learning approaches have gathered a lot of attention lately. In this work, we study their application to the breast cancer field, in particular for mass detection in mammograms. Several experiments were made on a real mammogram benchmark dataset. Deep Learning approaches were compared to other classification methodologies. It was concluded that,(More)
In this paper we present a complete system to detect breast cancer. Main objective is to support radiologists with automatic, semantic based, search methods directly over medical images. The complete step of microcalcification detection in mammography images is presented. We use a three stage algorithm that allows the detection and classification of(More)