Abstract
A multi-wavelengths analysis for pulse waveform extraction using laser speckle is conducted. The proposed system consists of three coherent light sources (532 nm, 635 nm, 850 nm). A bench-test composed of a moving skin-like phantom (silicone membrane) is used to compare the results obtained from different wavelengths. The system is able to identify a skin-like phantom vibration frequency, within physiological values, with a minimum error of 0.5 mHz for the 635 nm and 850 nm wavelengths and a minimum error of 1.3 mHz for the 532 nm light wavelength using a FFT-based algorithm. The phantom velocity profile is estimated with an error ranging from 27% to 9% using a bidimensional correlation coefficient-based algorithm. An in vivo trial is also conducted, using the 532 nm and 635 nm laser sources. The 850 nm light source has not been able to extract the pulse waveform. The heart rate is identified with a minimum error of 0.48 beats per minute for the 532 nm light source and a minimal error of 1.15 beats per minute for the 635 nm light source. Our work reveals that a laser speckle-based system with a 532 nm wavelength is able to give arterial pulse waveform with better results than those given with a 635 nm laser.

Abstract
Content-based image retrieval (CBIR) is a search technology that could aid medical diagnosis by retrieving and presenting earlier reported cases that are related to the one being diagnosed. To retrieve relevant cases, CBIR systems depend on supervised learning to map low-level image contents to high-level diagnostic concepts. However, the annotation by medical doctors for training and evaluation purposes is a difficult and time-consuming task, which restricts the supervised learning phase to specific CBIR problems of well-defined clinical applications. This paper proposes a new technique that automatically learns the similarity between the several exams from textual distances extracted from radiology reports, thereby successfully reducing the number of annotations needed. Our method first infers the relation between patients by using information retrieval techniques to determine the textual distances between patient radiology reports. These distances are subsequently used to supervise a metric learning algorithm, that transforms the image space accordingly to textual distances. CBIR systems with different image descriptions and different levels of medical annotations were evaluated, with and without supervision from textual distances, using a database of computer tomography scans of patients with interstitial lung diseases. The proposed method consistently improves CBIR mean average precision, with improvements that can reach 38%, and more marked gains for small annotation sets. Given the overall availability of radiology reports in picture archiving and communication systems, the proposed approach can be broadly applied to CBIR systems in different medical problems, and may facilitate the introduction of CBIR in clinical practice.

Abstract
Machine learning has been well applied and recognized as an effective tool to handle a wide range of real situations, including medical applications. In this scenario, it can help to alleviate problems typically suffered by researchers in this field, such as saving time for practitioners and providing unbiased results. This tutorial is concerned with the use of machine learning techniques to solve different medical problems. We provide a survey of recent methods developed or applied to this context, together with a review of novel contributions to the ESANN 2016 special session on Machine learning for medical applications.

Abstract

Abstract
Purpose: This paper presents the Rehab@home system, a tool specifically developed for helping neurological patients performing rehabilitation exercises at home, without the presence of a physiotherapist. It is centred on the rehabilitation of balance and on the sit-to-stand (STS) movement. Method: Rehab@home is composed of two Wii balance boards, a webcam and a computer, and it has two main software applications: one for patients to perform rehabilitation exercises and another one for therapists to visualize the data of the exercises. During the exercises, data from the boards and the webcam are processed in order to automatically assess the correctness of movements. Results: Rehab@home provides exercises for the rehabilitation of balance (in sitting and in standing positions), and for the execution of the STS movement. It gives automatic feedback to the patient and data are saved for future analysis. The therapist is able to adapt the difficulty of the exercises to match with each patient's needs. A preliminary study with seven patients was conducted for evaluating their feedback. They appreciated using the system and felt the exercises more engaging than conventional therapy. Conclusions: Feedback from patients gives the hope that Rehab@home can become a great tool for complementing their rehabilitation process.

Abstract
Over the past years, volumetric cardiac imaging has matured to a modality that can be used in daily routine clinical practice. However, despite the evolution of volumetric ultrasound systems with remarkable improvement in image quality, spatio-temporal resolution of the 3D data set remains limited and inferior to what can be obtained in 2D. Further development of volumetric ultrasound is thus required. However, the development of new beam forming techniques for 3D ultrasound (US) imaging requires an open, flexible and fully programmable US platform. To date, such systems are scarce and required (custom-made) dedicated electronics. Therefore, the aim of this report is to present a novel High channel Density Programmable ULtrasound System based on consumer Electronics (HD-PULSE). © 2015 IEEE.