Abstract
The hospitalization of patients with Heart Failure represents an increasing burden for the healthcare system with more than 23 million worldwide suffering from this disease. In this paper we explore methods to detect fluid retention in the lungs by measuring the thoracic impedance, so that is possible to monitor Heart Failure patients, and physicians can early detect acute episodes. A small and portable device was developed to measure the thoracic impedance of the patient. From the measured thoracic impedance it can estimate the accumulation of fluid in the lungs. This device is a low cost, friendly to use equipment that can be operated by a big range of users: Moreover, it was designed for low power consumption with a rechargeable battery for portable use. The device empowers the patient to monitor his own body fluid at home, and a physician can follow him remotely. This procedure would help to drastically reduce the number of hospitalizations and, consequently, improve the quality of life of people diagnosed with Heart Failure.

Abstract
Through the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs of cancer in mammograms. In these CAD systems, mass segmentation plays a central role in the decision process. In the literature, mass segmentation has been typically evaluated in a intra-sensor scenario, where the methodology is designed and evaluated in similar data. However, in practice, acquisition systems and PACS from multiple vendors abound and current works fails to take into account the differences in mammogram data in the performance evaluation. In this work it is argued that a comprehensive assessment of the mass segmentation methods requires the design and evaluation in datasets with different properties. To provide a more realistic evaluation, this work proposes: a) improvements to a state of the art method based on tailored features and a graph model; b) a head-to-head comparison of the improved model with recently proposed methodologies based in deep learning and structured prediction on four reference databases, performing a cross-sensor evaluation. The results obtained support the assertion that the evaluation methods from the literature are optimistically biased when evaluated on data gathered from exactly the same sensor and/or acquisition protocol.

Abstract

Abstract
Microscopic examination is currently the gold standard test for diagnosis of several neglected tropical diseases. However, reliable identification of parasitic infections requires in-depth train and access to proper equipment for subsequent microscopic analysis. These requirements are closely related with the increasing interest in the development of computer-aided diagnosis systems, and Mobile Health is starting to play an important role when it comes to health in Africa, allowing for distributed solutions that provide access to complex diagnosis even in rural areas. In this paper, we present a 3D-printed microscope that can easily be attached to a wide range of mobile devices models. To the best of our knowledge, this is the first proposed smartphone-based alternative to conventional microscopy that allows autonomous acquisition of a pre-defined number of images at 1000x magnification with suitable resolution, by using a motorized automated stage fully powered and controlled by a smartphone, without the need of manual focus of the smear slide. Reference smears slides with different parasites were used to test the device. The acquired images showed that was possible to visually detect those agents, which clearly illustrate the potential that this device can have, specially in developing countries with limited access to healthcare services.

Abstract

Abstract