Abstract
The enhancement of tubular structures such as vessels in medical images has been addressed in the past, aiming for easier extraction and or visualization of such structures by professionals. Some literature methodologies propose vesselness measures whose design is motivated by local properties of vascular networks and how these influence the eigenvalues of the Hessian matrix. However, past work fails to combine properly the scale-space and neighborhood information, thus leading to the proposal of suboptimal vesselness measures. In this paper, we show that a shallow convolutional neural network is able to learn more optimal embedding spaces from the eigenvalue analysis at different scales, thus leading to a stronger vessel enhancement. Additionally, we also show that such a system maintains one of the biggest advantages of Hessian-based vesselness measures, which is the robustness to data with varying statistics. © 2019, Springer Nature Switzerland AG.

Abstract
The performance of biometric systems is known to decay over time, eventually rendering them ineffective. Focused on ECG-based biometrics, this work aims to study the permanence of these signals for biometric identification in state-of-the-art methods, and measure the effect of template update on their long-term performance. Ensuring realistic testing settings, four literature methods based on autocorrelation, autoencoders, and discrete wavelet and cosine transforms, were evaluated with and without template update, using Holter signals from THEW’s E-HOL 24 h database. The results reveal ECG signals are unreliable for long-term biometric applications, and template update techniques offer considerable improvements over the state-of-the-art results. Nevertheless, further efforts are required to ensure long-term effectiveness in real applications. © 2019, Springer Nature Switzerland AG.

Abstract
Transfer learning focuses on building better predictive models by exploiting knowledge gained in previous related tasks, being able to soften the traditional supervised learning assumption of having identical train-test distributions. Most efforts on transfer learning consider revisiting the data from the source tasks or rely on transferring knowledge for specific models. In this paper, a general framework is proposed for transferring knowledge by including a regularization factor based on the structural model similarity between related tasks. The proposed approach is instantiated to different models for regression, classification, ranking and recommender systems, obtaining competitive results in all of them. Also, we explore high-level concepts in transfer learning like sparse transfer, partially observable transfer and cross-model transfer.

Abstract
Breast cancer high survival rate led to an increased interest in the quality of life after treatment, particularly regarding the aesthetic outcome. Currently used aesthetic assessment methods are subjective, which make reproducibility and impartiality impossible. To create an objective method capable of being selected as the gold standard, it is fundamental to detect, in a completely automatic manner, keypoints in photographs of women's torso after being subjected to breast cancer surgeries. This paper proposes a deep and a hybrid model to detect keypoints with high accuracy. Our methods are tested on two datasets, one composed of images with a clean and consistent background and a second one that contains photographs taken under poor lighting and background conditions. The proposed methods represent an improvement in the detection of endpoints, nipples and breast contour for both datasets in terms of average error distance when compared with the current state-of-the-art.

Abstract
In order to increase the adoption of machine learning models in areas like medicine and finance, it is necessary to have correct and diverse explanations for the decisions that the models provide, to satisfy the curiosity of decision-makers and the needs of the regulators. In this paper, we introduced a method, based in a previously presented framework, to explain the decisions of an Ensemble Model. Moreover, we instantiate the proposed approach to an ensemble composed of a Scorecard, a Random Forest, and a Deep Neural Network, to produce accurate decisions along with correct and diverse explanations. Our methods are tested on two biomedical datasets and one financial dataset. The proposed ensemble leads to an improvement in the quality of the decisions, and in the correctness of the explanations, when compared to its constituents alone. Qualitatively, it produces diverse explanations that make sense and convince the experts.

Abstract
Overhead Power Distribution Lines (OPDLs) correspond to a large percentage of the medium-voltage electrical systems. In these networks, visual inspection activities are usually performed without resorting to automated systems, requiring a significant investment of time and human resources. We present a methodology to identify the defect and type of insulators using Convolutional Neural Networks (CNNs). More than 2500 photographs were collected both from inside a studio and from a realistic OPDL. A classification model is proposed to automatically recognize the insulators conformity. This model is able to learn from indoors photographs by augmenting these images with realistic details such as top ties and real-world backgrounds. Furthermore, Multi-Task Learning (MTL) was used to improve performance of defect detection by also predicting the insulator class. The proposed methodology is able to achieve an accuracy of 92% for material classification and 85% for defect detection, with F1-score of 0.75, surpassing available solutions.