Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker.

Acute leukemia is a cancer-related to a bone marrow abnormality. It is more common in children and young adults. This type of leukemia generates unusual cell growth in a short period, requiring a quick start of treatment. Acute Lymphoid Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the main responsible for deaths caused by this cancer. The classification of these two leukemia types on blood slide images is a vital process of and automatic system that can assist doctors in the selection of appropriate treatment. This work presents a convolutional neural networks (CNNs) architecture capable of differentiating blood slides with ALL, AML and Healthy Blood Slides (HBS). The experiments were performed using 16 datasets with 2,415 images, and the accuracy of 97.18% and a precision of 97.23% were achieved. The proposed model results were compared with the results obtained by the state of the art methods, including also based on CNNs.Index Terms-leukemia diagnosis, convolutional neural network, computer aided diagnosis.

Purpose: The present review article aims to provide an overview of the available radionuclides for palliative treatment of bone metastases beyond 89 Sr and 153 Sm. In addition, it aims to review and summarize the clinical outcomes associated with the palliative treatment of bone metastases using different radiopharmaceuticals. Materials and Methods: A literature search was conducted on Science Direct and PubMed databases (1990 -2015). The following search terms were combined in order to obtain relevant results: "bone", "metastases", "palliative", "care", "therapy", "treatment", "radiotherapy", "review", "radiopharmaceutical", "phosphorus-32", "strontium-89", "yttrium-90", "tin-117m", "samarium-153", "holmium-166", "thulium-170", "lutetium-177", "rhenium-186", "rhenium-188" and "radium-223". Studies were included if they provided information regarding the clinical outcomes. Results and Conclusions: A comparative analysis of the measured therapeutic response of different radiopharmaceuticals, based on previously published data, suggests that there is a lack of substantial differences in palliative efficacy among radiopharmaceuticals. However, when the comparative analysis adds factors such as patient's life expectancy, radionuclides' physical characteristics (e.g. tissue penetration range and half-life) and health economics to guide the rational selection of a radiopharmaceutical for palliative treatment of bone metastases, 177 Lu and 188 Re-labeled radiopharmaceuticals appear to be the most suitable radiopharmaceuticals for treatment of small and medium/large size bone lesions, respectively.

Cluster computing is presently a major research area, mostly for high performance computing.

The classification of imbalanced datasets, especially in medicine, is a major problem in data mining. Such a problem is evident in analyzing normal and abnormal subjects about knee from data collected during walking. In this work, surface electromyography (sEMG) data were collected during walking from the lower limb of 22 individuals (11 with and 11 without knee abnormality). Subjects with a knee abnormality take longer to complete the walking task than healthy subjects. Therefore, the SEMG signal length of unhealthy subjects is longer than that of healthy subjects, resulting in a problem of imbalance in the collected sEMG signal data. Thus, the development of a classification model for such datasets is challenging due to the bias towards the majority class in the data. The collected sEMG signals are challenging due to the contribution of multiple motor units at a time and their dependency on neuromuscular activity, physiological and anatomical properties of the involved muscles. Hence, automated analysis of such sEMG signals is an arduous task. A multi-step classification scheme is proposed in this research to overcome this limitation. The Wavelet Denoising (WD) scheme is used to denoise the collected sEMG signals, followed by the extraction of eleven time-domain features. The oversampling techniques are then used to balance the data under analysis

One of the critical challenges facing 3D video systems and images such as holography lies in their compression technique. High-efficiency video coding (HEVC) has emerged as one of the leading schemes to address this challenge. In this article, a novel method based on wavelet transform is presented to improve HEVC, particularly in digital holography systems (object plane). In this regard, wavelet and resizing are included in the coding process, while extra HEVC decoders and encoders are added to predict and decrease errors in the target. Simulation results reveals that the proposed algorithm reduces Bjøntegaard-Delta (BD) bitrate 17.5% (based on average BD-Rate values) compared to the original HEVC (H.265) scheme while maintaining signal fidelity and even enhancing it slightly. We observe an increased BDpeak-signal-to-noise ratio (BD-PSNR) in real and imaginary parts of digital holograms of high rate quantization values up to 1.1 dB.

Abstract. Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information from the model representation leads to better generalization. We tested our hypothesis for the problem of mitosis detection in breast cancer histopathology images and made a comparative analysis with two other approaches. We show that combining color augmentation with domain-adversarial training is a better alternative than standard approaches to improve the generalization of deep learning methods.

Neste artigo será apresentado um sistema, de análise e processamento de imagem e de computação gráfica, que tem vindo a ser desenvolvido no Laboratório Sinal e Imagem do INEB – Instituto de Engenharia Biomédica desde 1995. A opção pelo desenvolvimento de uma nova aplicação, em vez da adaptação de uma aplicação já existente, deveu-se à constatação, por parte dos seus autores, da falta de plataformas que fossem adequadas não só para serem utilizadas como pacotes fechados, mas também como aplicações abertas que facilmente permitissem a incorporação de novas funções. O desejo do sistema incluir funções comuns na área da análise e processamento de imagem, em conjunto com algumas ferramentas habitualmente existentes no domínio da computação gráfica, e a necessária possibilidade de controlar comodamente as condições de ensaio e de análise dos resultados obtidos por novos algoritmos, foram também argumentos importantes que condicionaram a decisão do desenvolvimento de um novo sistema.