Bio: Bart Jansen is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Imitation & Rehabilitation. The author has an hindex of 22, co-authored 131 publications receiving 2770 citations. Previous affiliations of Bart Jansen include iMinds & VU University Amsterdam.
Papers published on a yearly basis
TL;DR: The LUNA16 challenge is described, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set, and the results so far are presented.
Abstract: Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.
TL;DR: The analyses presented in this study can be used as a guideline for the prediction of negative outcomes according to the frailty concept used, as well as to estimate the time frame within which these events can be expected to occur.
Abstract: INTRODUCTION Frailty is one of the most important concerns regarding our aging population. Evidence grows that the syndrome is linked to several important health outcomes. A general overview of frailty concepts and a comprehensive meta-analysis of their relation with negative health outcomes still lacks in literature, making it difficult for health care professionals and researchers to recognize frailty and the related health risks on the one hand and on the other hand to appropriately follow up the frailty process and take substantiated action. Therefore, this study aims to give an overview of the predictive value of the main frailty concepts for negative health outcomes in community-dwelling older adults. METHODS This review and meta-analysis assembles prospective studies regarding the relation between frailty and any potential health outcome. Frailty instruments were subdivided into frailty concepts, so as to make comprehensive comparisons. Odds ratios (ORs), hazard ratios (HRs), and relative risk (RR) scores were extracted from the studies, and meta-analyses were conducted in OpenMeta Analyst software. RESULTS In total, 31 articles retrieved from PubMed, Web of Knowledge, and PsycInfo provided sufficient information for the systematic review and meta-analysis. Overall, (pre)frailty increased the likelihood for developing negative health outcomes; for example, premature mortality (OR 2.34 [1.77-3.09]; HR/RR 1.83 [1.68-1.98]), hospitalization (OR 1.82 [1.53-2.15]; HR/RR 1.18 [1.10-1.28]), or the development of disabilities in basic activities of daily living (OR 2.05 [1.73-2.44]); HR/RR 1.62 [1.50-1.76]). CONCLUSION Overall, frailty increases the risk for developing any discussed negative health outcome, with a 1.8- to 2.3-fold risk for mortality; a 1.6- to 2.0-fold risk for loss of activities of daily living; 1.2- to 1.8-fold risk for hospitalization; 1.5- to 2.6-fold risk for physical limitation; and a 1.2- to 2.8-fold risk for falls and fractures. The analyses presented in this study can be used as a guideline for the prediction of negative outcomes according to the frailty concept used, as well as to estimate the time frame within which these events can be expected to occur.
TL;DR: Xbox Kinect reproducibility was found to be statistically similar to MBS results for the four exercises, however, measured ROMs however were found different between the systems.
Abstract: The recent availability of the Kinect™ sensor, a cost-effective markerless motion capture system (MLS), offers interesting possibilities in clinical functional analysis and rehabilitation. However, neither validity nor reproducibility of this device is known yet. These two parameters were evaluated in this study. Forty-eight volunteers performed shoulder abduction, elbow flexion, hip abduction and knee flexion motions; the same protocol was repeated one week later to evaluate reproducibility. Movements were simultaneously recorded by the Kinect (with Microsoft Kinect SDK v.1.5) MLS and a traditional marker-based stereophotogrammetry system (MBS). Considering the MBS as reference, discrepancies between MLS and MBS were evaluated by comparing the range of motion (ROM) between both systems. MLS reproducibility was found to be statistically similar to MBS results for the four exercises. Measured ROMs however were found different between the systems.
TL;DR: A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem and the overall performance of the CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.
Abstract: Purpose: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. Methods: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. Results: The overall performancemore » of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. Conclusions: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.« less
TL;DR: The results of this review show that in most cases, the introduction of VG training in physical rehabilitation offered similar results as conventional therapy, and VGs could be added as an adjunct treatment in rehabilitation for various pathologies to stimulate patient motivation.
Abstract: The aim of this paper was to investigate the effect of commercial video games (VGs) in physical rehabilitation of motor functions. Several databases were screened (Medline, SAGE Journals Online, and ScienceDirect) using combinations of the following free-text terms: commercial games, video games, exergames, serious gaming, rehabilitation games, PlayStation, Nintendo, Wii, Wii Fit, Xbox, and Kinect. The search was limited to peer-reviewed English journals. The beginning of the search time frame was not restricted and the end of the search time frame was 31 December 2015. Only randomized controlled trial, cohort, and observational studies evaluating the effect of VGs on physical rehabilitation were included in the review. A total of 4728 abstracts were screened, 275 were fully reviewed, and 126 papers were eventually included. The following information was extracted from the selected studies: device type, number and type of patients, intervention, and main outcomes. The integration of VGs into physical rehabilitation has been tested for various pathological conditions, including stroke, cerebral palsy, Parkinson's disease, balance training, weight loss, and aging. There was large variability in the protocols used (e.g. number of sessions, intervention duration, outcome measures, and sample size). The results of this review show that in most cases, the introduction of VG training in physical rehabilitation offered similar results as conventional therapy. Therefore, VGs could be added as an adjunct treatment in rehabilitation for various pathologies to stimulate patient motivation. VGs could also be used at home to maintain rehabilitation benefits.
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
TL;DR: In this article, a wide list of topics ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading are reviewed and connections between these problems and other, more traditional, topics of statistical physics are highlighted.
Abstract: Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.
TL;DR: A comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings, which analyzes and categorizes the multiple ways in which examples are gathered, as well as the various techniques for policy derivation.
Abstract: We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans To conclude we discuss LfD limitations and related promising areas for future research
01 Jan 2007