Abstract
In this work, we present a method to separate aortic (A2) and pulmonary (P2) components from second heart sounds (S2). The proposed approach captures the different dynamical behavior of A2 and P2 components via a joint Gaussian mixture model, which is then used to perform separation via a closed-form conditional mean estimator.The proposed approach is tested over synthetic heart sounds and it is shown guarantee a reduction of approximately 25% of the normalized root mean-squared error incurred in signal separation, with respect to a previously presented approach in the literature. © 2019 Creative Commons.

Abstract
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art.

Abstract
This paper studies the use of non-invasive acoustic emission recordings for clinical device tracking. In particular, audio signals recorded at the proximal end of a needle are used to detect perforation events that occur when the needle tip crosses internal tissue layers. A comparative study is performed to assess the capacity of different features and envelopes in detecting perforation events. The results obtained from the considered experimental setup show a statistically significant correlation between the extracted envelopes and the perforation events, thus leading the way for future development of perforation detection algorithms.

Abstract

Abstract
The teaching process of auscultation is complex in itself, and difficult to operate since it requires a wide spectrum of patients with the most diverse cardiopulmonary pathologies, readily available during teaching and assessment hours, for an ever-growing number of medical students. In this paper we will focus on how virtual patient technologies can promote the evolution of the current teaching methodologies, promoting better learning. The chosen methodology was: a) a review of available medical simulation technologies for auscultation teaching; b) a case study illustrating how a virtual patient simulation technology has been successfully used to teach and certify auscultation skills. Results show the positive impact and high acceptability of virtual patient simulation technologies in the teaching of auscultation to medical students. © 2020 IEEE.

Abstract
In this paper, we consider the problem of classifying skin lesions into multiple classes using both dermoscopic and clinical images. Different convolutional neural network architectures are considered for this task and a novel ensemble scheme is proposed, which makes use of a progressive transfer learning strategy. The proposed approach is tested over a dataset of 4000 images containing both dermoscopic and clinical examples and it is shown to achieve an average specificity of 93.3% and an average sensitivity of 79.9% in discriminating skin lesions belonging to four different classes.