Learning to detect chest radiographs containing lung nodules using visual attention networks

Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak labels through convolutional neural networks for radiograph classification. We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available. Annotated nodules are used at training time to deliver a visual attention mechanism informing the model about its localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the estimated position of a nodule against the ground truth, when this is available. A corresponding localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning. When a nodule annotation is available at training time, the reward function is modified accordingly so that exploring portions of the radiographs away from a nodule incurs a larger penalty. Our empirical results demonstrate the potential advantages of these architectures in comparison to competing methodologies

Brain growth and development in fetuses with congenital heart disease

Introduction and Objectives: In the current era of excellent surgical results for congenital heart disease (CHD), focus has become directed on quality of life for these children. Previous studies have shown that neurodevelopmental outcome in CHD is impaired. The mechanisms are incompletely understood but there is increasing evidence that the origins of this are in fetal life. This thesis aims to describe the in utero brain growth in a cohort of fetuses with CHD and relate this to the circulatory abnormalities and fetal Doppler parameters. Methods: Pregnant women with a fetus with CHD were prospectively recruited. The congenital heart defect was phenotyped using fetal echocardiography and patients subdivided into three physiological groups on the basis of the anticipated abnormality of cerebral blood flow and oxygen delivery: (1) isolated reduced flow to the brain; 2) reduced oxygen saturation of cerebral blood flow; (3) combination of reduced oxygen and flow. Fetal brain MRI was performed. In addition to standard biometric measurements, snapshot to volume reconstruction (SVR) was used to construct a 3D data set from the oversampled raw data. From these 3D volumes the total brain volume and ventricular volumes were measured by manual segmentation. Serial measurements of fetal growth were also made and umbilical artery and middle cerebral artery Doppler parameters were analysed. Results: 29 women were included; comparison was made with 83 normal MRI controls. Fetuses with CHD were found to have smaller brain volumes compared to controls when adjusting for advancing gestation (p<0.01). This difference becomes more pronounced with advancing gestation, suggesting a slower rate of in utero brain growth. Measurements of growth found that the fetuses with CHD were smaller throughout gestation with a highly significant difference at the later growth scan. (p<0.001). Cerebral and umbilical artery Doppler data showed evidence of reduced cerebrovascular resistance in fetuses with CHD but did not show a difference in the umbilical artery Doppler. Conclusion: Fetuses with CHD have evidence of impaired brain growth with advancing pregnancy and an increased rate of overall growth restriction. Doppler evidence of cerebral vasodilation supports the mechanism of reduced oxygen delivery as an underlying cause.Open Acces

A Review of EMG Techniques for Detection of Gait Disorders

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices

Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation

Clinical decision support systems (clinical DSSs) are widely used today for various clinical applications such as diagnosis, treatment, and recovery. Clinical DSS aims to enhance the end‐to‐end therapy management for the doctors, and also helps to provide improved experience for patients during each phase of the therapy. The goal of this chapter is to provide an insight into the clinical DSS associated with the highly prevalent heart rhythm disorder, atrial fibrillation (AF). The use of clinical DSS in AF management is ubiquitous, starting from detection of AF through sophisticated electrophysiology treatment procedures, all the way to monitoring the patient\u27s health during follow‐ups. Most of the software associated with AF DSS are developed based on signal processing, image processing, and artificial intelligence techniques. The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. The chapter continues with a background on AF and some relevant mechanisms. Finally, a couple of clinical DSS used today in regard with AF are discussed, along with some proposed methods for potential implementation of clinical DSS for detection of AF, prediction of an AF treatment outcome, and localization of AF targets during a treatment procedure

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft