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Author F.Negin; Pau Rodriguez; M.Koperski; A.Kerboua; Jordi Gonzalez; J.Bourgeois; E.Chapoulie; P.Robert; F.Bremond edit  url
openurl 
  Title PRAXIS: Towards automatic cognitive assessment using gesture recognition Type Journal Article
  Year 2018 Publication Expert Systems with Applications Abbreviated Journal ESWA  
  Volume 106 Issue Pages 21-35  
  Keywords  
  Abstract Praxis test is a gesture-based diagnostic test which has been accepted as diagnostically indicative of cortical pathologies such as Alzheimer’s disease. Despite being simple, this test is oftentimes skipped by the clinicians. In this paper, we propose a novel framework to investigate the potential of static and dynamic upper-body gestures based on the Praxis test and their potential in a medical framework to automatize the test procedures for computer-assisted cognitive assessment of older adults.

In order to carry out gesture recognition as well as correctness assessment of the performances we have recollected a novel challenging RGB-D gesture video dataset recorded by Kinect v2, which contains 29 specific gestures suggested by clinicians and recorded from both experts and patients performing the gesture set. Moreover, we propose a framework to learn the dynamics of upper-body gestures, considering the videos as sequences of short-term clips of gestures. Our approach first uses body part detection to extract image patches surrounding the hands and then, by means of a fine-tuned convolutional neural network (CNN) model, it learns deep hand features which are then linked to a long short-term memory to capture the temporal dependencies between video frames.
We report the results of four developed methods using different modalities. The experiments show effectiveness of our deep learning based approach in gesture recognition and performance assessment tasks. Satisfaction of clinicians from the assessment reports indicates the impact of framework corresponding to the diagnosis.
 
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  Call Number Admin @ si @ NRK2018 Serial (up) 3669  
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Author O.F.Ahmad; Y.Mori; M.Misawa; S.Kudo; J.T.Anderson; Jorge Bernal edit  url
doi  openurl
  Title Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method Type Journal Article
  Year 2021 Publication Endoscopy Abbreviated Journal END  
  Volume 53 Issue 9 Pages 893-901  
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  Abstract BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.  
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  Notes ISE Approved no  
  Call Number Admin @ si @ AMM2021 Serial (up) 3670  
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Author Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez edit  doi
openurl 
  Title Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition Type Journal Article
  Year 2022 Publication ACM Transactions on Multimedia Computing, Communications, and Applications Abbreviated Journal ACMTMC  
  Volume Issue Pages  
  Keywords Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition  
  Abstract Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.  
  Address May 2022  
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  Notes ISE; 600.157 Approved no  
  Call Number Admin @ si @ GYW2022 Serial (up) 3692  
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Author Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach edit  url
doi  openurl
  Title Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge Type Journal Article
  Year 2022 Publication Gastrointestinal Endoscopy Abbreviated Journal  
  Volume 96 Issue 2 Pages 370-372  
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  Notes ISE Approved no  
  Call Number Admin @ si @ MMB2022 Serial (up) 3701  
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Author Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez edit  doi
openurl 
  Title Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem Type Journal
  Year 2020 Publication Mathematics Abbreviated Journal MATH  
  Volume 20 Issue 8(9) Pages 1595  
  Keywords STEM education; Project-based learning; Coding; software tool  
  Abstract In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view.
 
  Address September 2020  
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  Notes IAM; ISE Approved no  
  Call Number Admin @ si @ Serial (up) 3722  
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