: 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

Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.

Background and objective: Contrast-enhanced spectral mammography (CESM) is a new imaging modality that integrates digital mammography and the use of iodinated contrast agents in a dual-energy acquisition protocol, facilitating the differentiation between benign and malignant breast lesions. However, its clinical use is constrained by the potential risks associated with iodinated contrast agents and the increased radiation exposure. Method: The proposed strategy adapts a self-calibrated pixel attention (SCPA) mechanism to enhance the ability of conditional image-to-image translation models to synthesize contrast uptake in breast lesions. To assess the generalizability and feasibility of our approach under realistic data variations, we used two independent CESM datasets. The performance of the model was evaluated through quantitative and qualitative analyses, considering both whole-image performance and contrast-uptake regions. The results were compared with state-of-the-art conditional generative models. Furthermore, the clinical reliability of the images synthesized by the proposed strategy was assessed through a qualitative analysis performed by an expert breast radiologist. Results: The proposed strategy outperformed state-of-the-art approaches in the synthesis of both whole images and contrast-enhanced regions. In particular, the SCPA-conditional generative adversarial network exhibited superior preservation of the morphology of the lesion while maintaining the overall quality of the generated images. Moreover, the clinical feasibility evaluation showed acceptable realism and technical quality suitable for radiologist interpretation, although in some cases, residual synthesis errors were observed that could affect patient management. Conclusion: The integration of attention mechanisms substantially improves the synthesis of contrast uptake in regions of interest (ROIs) within CESM studies with limited and heterogeneous datasets. These findings demonstrate the feasibility of generating contrast enhancement from non-contrast mammography images, highlighting the potential of this approach to reduce the need for contrast administration while preserving clinically relevant diagnostic information. This work should therefore be regarded as a feasibility study that establishes a clinically aligned ROI-focused framework for CESM virtual contrast synthesis and provides a foundation for future work.