: magnetic resonance imaging (MRI) has been increasingly used to study breast cancer for screening high-risk cases, pre-operative staging, and problem-solving because of its high sensitivity. However, its cost-effectiveness is still debated. Thus, the concept of abbreviated MRI (ABB-MRI) protocols was proposed as a possible solution for reducing MRI costs : to investigate the role of the abbreviated MRI protocols in detecting and staging breast cancer : a systematic search of the literature was carried out in the bibliographic databases: Scopus, PubMed, Medline, and Science Direct. : forty-one articles were included, which described results of the assessment of fifty-three abbreviated protocols for screening, staging, recurrence assessing, and problem-solving or clarification. : the use of ABB-MRI protocols allows reducing the acquisition and reading times, maintaining a high concordance with the final interpretation, in comparison to a complete protocol. However, larger prospective and multicentre trials are necessary to validate the performance in specific clinical environments

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively.

Deep learning methods have resulted in effective strategies for improving performance in a large number of applications, becoming one of the most used strategies by developers and researchers. In order to facilitate the implementation of those approaches, a set of software frameworks have been developed and are currently available. Selection of a specific framework is an important task, especially when computational resources are limited. In order to provide information for deciding it, this paper presents a comparative study of three of the most widely used deep learning frameworks namely Tensorflow, Theano and Torch. The comparison is carried out by implementing convolutional and recurrent architectures for classifying images from two databases: MNIST and aFAR-10. Computational costs i.e. gradient computation time, forward time and memory consumption are reported for both GPU and CPU settings.