Supervised Nonlinear Unmixing of Hyperspectral Images Using a Pre-image Methods

This book is a collection of 19 articles which reflect the courses given at the Collège de France/Summer school “Reconstruction d'images − Applications astrophysiques“ held in Nice and Fréjus, France, from June 18 to 22, 2012. The articles presented in this volume address emerging concepts and methods that are useful in the complex process of improving our knowledge of the celestial objects, including Earth

Postural adaptations to unilateral knee joint hypomobility induced by orthosis wear during gait initiation

Abstract Balance control and whole-body progression during gait initiation (GI) involve knee-joint mobility. Single knee-joint hypomobility often occurs with aging, orthopedics or neurological conditions. The goal of the present study was to investigate the capacity of the CNS to adapt GI organization to single knee-joint hypomobility induced by the wear of an orthosis. Twenty-seven healthy adults performed a GI series on a force-plate in the following conditions: without orthosis ("control"), with knee orthosis over the swing leg ("orth-swing") and with the orthosis over the contralateral stance leg ("orth-stance"). In orth-swing, amplitude of mediolateral anticipatory postural adjustments (APAs) and step width were larger, execution phase duration longer, and anteroposterior APAs smaller than in control. In orth-stance, mediolateral APAs duration was longer, step width larger, and amplitude of anteroposterior APAs smaller than in control. Consequently, step length and progression velocity (which relate to the "motor performance") were reduced whereas stability was enhanced compared to control. Vertical force impact at foot-contact did not change across conditions, despite a smaller step length in orthosis conditions compared to control. These results show that the application of a local mechanical constraint induced profound changes in the global GI organization, altering motor performance but ensuring greater stability

Survey instruments used in clinical and epidemiological research on waterpipe tobacco smoking: a systematic review

<p>Abstract</p> <p>Background</p> <p>The primary objective was to systematically review the medical literature for instruments validated for use in epidemiological and clinical research on waterpipe smoking.</p> <p>Methods</p> <p>We searched the following databases: MEDLINE, EMBASE, and ISI the Web of Science. We selected studies using a two-stage duplicate and independent screening process. We included papers reporting on the development and/or validation of survey instruments to measure waterpipe tobacco consumption or related concepts. Two reviewers used a standardized and pilot tested data abstraction form to collect data from each eligible study using a duplicate and independent screening process. We also determined the percentage of observational studies assessing the health effects of waterpipe tobacco smoking and the percentage of studies of prevalence of waterpipe tobacco smoking that have used validated survey instruments.</p> <p>Results</p> <p>We identified a total of five survey instruments. One instrument was designed to measure knowledge, attitudes, and waterpipe use among pregnant women and was shown to have internal consistency and content validity. Three instruments were designed to measure waterpipe tobacco consumption, two of which were reported to have face validity. The fifth instrument was designed to measure waterpipe dependence and was rigorously developed and validated. One of the studies of prevalence and none of the studies of health effects of waterpipe smoking used validated instruments.</p> <p>Conclusions</p> <p>A number of instruments for measuring the use of and dependence on waterpipe smoking exist. Future research should study content validity and cross cultural adaptation of these instruments.</p

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

The prevalence of waterpipe tobacco smoking among the general and specific populations: a systematic review

Abstract Background The objective of this study was to systematically review the medical literature for the prevalence of waterpipe tobacco use among the general and specific populations. Methods We electronically searched MEDLINE, EMBASE, and the ISI the Web of Science. We selected studies using a two-stage duplicate and independent screening process. We included cohort studies and cross sectional studies assessing the prevalence of use of waterpipe in either the general population or a specific population of interest. Two reviewers used a standardized and pilot tested form to collect data from each eligible study using a duplicate and independent screening process. We stratified the data analysis by country and by age group. The study was not restricted to a specific context. Results Of a total of 38 studies, only 4 were national surveys; the rest assessed specific populations. The highest prevalence of current waterpipe smoking was among school students across countries: the United States, especially among Arab Americans (12%-15%) the Arabic Gulf region (9%-16%), Estonia (21%), and Lebanon (25%). Similarly, the prevalence of current waterpipe smoking among university students was high in the Arabic Gulf region (6%), the United Kingdom (8%), the United States (10%), Syria (15%), Lebanon (28%), and Pakistan (33%). The prevalence of current waterpipe smoking among adults was the following: Pakistan (6%), Arabic Gulf region (4%-12%), Australia (11% in Arab speaking adults), Syria (9%-12%), and Lebanon (15%). Group waterpipe smoking was high in Lebanon (5%), and Egypt (11%-15%). In Lebanon, 5%-6% pregnant women reported smoking waterpipe during pregnancy. The studies were all cross-sectional and varied by how they reported waterpipe smoking. Conclusion While very few national surveys have been conducted, the prevalence of waterpipe smoking appears to be alarmingly high among school students and university students in Middle Eastern countries and among groups of Middle Eastern descent in Western countries

A Gaussian Process Regression Approach for Testing Granger Causality between Time Series Data

Granger causality considers the question of whether two time series exert causal influences on each other. Causality testing usually relies on prediction, i.e., if the prediction error of the first time series is reduced by taking measurements from the second one into account, then the latter is said to have a causal influence on the former. In this paper, a nonparametric framework based on functional estimation is proposed. Nonlinear prediction is performed via the Bayesian paradigm, using Gaussian processes. Some experiments illustrate the efficiency of the approach. Index Terms — Granger causality, functional estimation, Gaussian process, reproducing kernel 1

SUPERVISED NONLINEAR UNMIXING OF HYPERSPECTRAL IMAGES USING A PRE-IMAGE METHODS

Abstract. Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. This involves the decomposition of each mixed pixel into its pure endmember spectra, and the estimation of the abundance value for each endmember. Although linear mixture models are often considered because of their simplicity, there are many situations in which they can be advantageously replaced by nonlinear mixture models. In this chapter, we derive a supervised kernel-based unmixing method that relies on a pre-image problem-solving technique. The kernel selection problem is also briefly considered. We show that partially-linear kernels can serve as an appropriate solution, and the nonlinear part of the kernel can be advantageously designed with manifold-learning-based techniques. Finally, we incorporate spatial information into our method in order to improve unmixing performance.

Multiple Instance Learning for Histopathological Breast Cancer Images

International audienceHistopathological images are the gold standard for breast cancer diagnosis. During examination several dozens of them are acquired for a single patient. Conventional, image-based classification systems make the assumption that all the patient’s images have the same label as the patient, which is rarely verified in practice since labeling the data is expensive. We propose a weakly supervised learning framework and investigate the relevance of Multiple Instance Learning (MIL) for computer-aided diagnosis of breast cancer patients, based on the analysis of histopathological images. Multiple instance learning consists in organizing instances (images) into bags (patients), without the need to label all the instances. We compare several state-of-the-art MIL methods including the pioneering ones (APR, Diverse Density, MI-SVM, citation-kNN), and more recent ones such as a non parametric method and a deep learning based approach (MIL-CNN). The experiments are conducted on the public BreaKHis dataset which contains about 8000 microscopic biopsy images of benign and malignant breast tumors, originating from 82 patients. Among the MIL methods the non-parametric approach has the best overall results, and in some cases allows to obtain classification rates never reached by conventional (single instance) classification frameworks. The comparison between MIL and single instance classification reveals the relevance of the MIL paradigm for the task at hand. In particular, the MIL allows to obtain comparable or better results than conventional (single instance) classification without the need to label all the images

Multiple Instance Learning for Histopathological Breast Cancer Images

International audienceHistopathological images are the gold standard for breast cancer diagnosis. During examination several dozens of them are acquired for a single patient. Conventional, image-based classification systems make the assumption that all the patient’s images have the same label as the patient, which is rarely verified in practice since labeling the data is expensive. We propose a weakly supervised learning framework and investigate the relevance of Multiple Instance Learning (MIL) for computer-aided diagnosis of breast cancer patients, based on the analysis of histopathological images. Multiple instance learning consists in organizing instances (images) into bags (patients), without the need to label all the instances. We compare several state-of-the-art MIL methods including the pioneering ones (APR, Diverse Density, MI-SVM, citation-kNN), and more recent ones such as a non parametric method and a deep learning based approach (MIL-CNN). The experiments are conducted on the public BreaKHis dataset which contains about 8000 microscopic biopsy images of benign and malignant breast tumors, originating from 82 patients. Among the MIL methods the non-parametric approach has the best overall results, and in some cases allows to obtain classification rates never reached by conventional (single instance) classification frameworks. The comparison between MIL and single instance classification reveals the relevance of the MIL paradigm for the task at hand. In particular, the MIL allows to obtain comparable or better results than conventional (single instance) classification without the need to label all the images