Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. However, it can often be cured by removing the affected tissues when detected in early stages. Therefore, it is relevant to provide universal and efficient access to cervical screening programs, being digital colposcopy an inexpensive technique with high potential of scalability. The development of computer-aided diagnosis systems for the automated processing of digital colposcopies has gained the attention of the computer vision and machine learning communities in the last decade, giving origin to a wide diversity of tasks and computational solutions. However, there is a lack of a unified framework to discuss the main tasks and to assess their performance. Thus, in this paper, we studied the core research lines surrounding the automated analysis of digital colposcopies and built a topology of problems and techniques, including their key properties, advantages, and limitations. Also, we discussed the open challenges in the area and released a database that serves as a common basis to evaluate such systems.

Abstract
Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.

Abstract

Abstract
Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

Abstract
The majority of computer-Aided diagnosis methods for Alzheimer's disease (AD) from brain images either address only two stages of the disease at a time (and reduce the problem to binary classification) or do not exploit the ordinal nature of the different classes. An exception is the work by Fan et al. [1], which proposed an ordinal method that obtained better performance than traditional multiclass classification. Still, special care should be taken when data is class imbalanced, i.e. when some classes are overly represented when compared to others. Building on top of [1], this work makes use of a recently published ordinal classifier, which transforms the problem into sets of pairwise ranking problems, in order to address the class imbalance in the data [2]. Several methods were experimented with, using a Support Vector Machine as the underlying estimator. The pairwise ranking approach has shown promising results, both for traditional and imbalance metrics. © 2018 IEEE.

Abstract
Purpose: BCCT.core (Breast Cancer Conservative Treatment. cosmetic results) is a software created for the objective evaluation of aesthetic result of breast cancer conservative treatment using a single patient frontal photography. The lack of volume information has been one criticism, as the use of 3D information might improve accuracy in aesthetic evaluation. In this study, we have evaluated the added value of 3D information to two methods of aesthetic evaluation: a panel of experts; and an augmented version of the computational model - BCCT.core3d. Material and methods: Within the scope of EU Seventh Framework Programme Project PICTURE, 2D and 3D images from 106 patients from three clinical centres were evaluated by a panel of 17 experts and the BCCT.core. Agreement between all methods was calculated using the kappa (K) and weighted kappa (wK) statistics. Results: Subjective agreement between 2D and 3D individual evaluation was fair to moderate. The agreement between the expert classification and the BCCT.core software with both 2D and 3D features was also fair to moderate. Conclusions: The inclusion of 3D images did not add significant information to the aesthetic evaluation either by the panel or the software. Evaluation of aesthetic outcome can be performed using of the BCCT.core software, with a single frontal image.