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Author Frederic Sampedro; Anna Domenech; Sergio Escalera edit  url
doi  openurl
  Title Static and dynamic computational cancer spread quantification in whole body FDG-PET/CT scans Type Journal Article
  Year 2014 Publication Journal of Medical Imaging and Health Informatics Abbreviated Journal JMIHI  
  Volume (up) 4 Issue 6 Pages 825-831  
  Keywords CANCER SPREAD; COMPUTER AIDED DIAGNOSIS; MEDICAL IMAGING; TUMOR QUANTIFICATION  
  Abstract In this work we address the computational cancer spread quantification scenario in whole body FDG-PET/CT scans. At the static level, this setting can be modeled as a clustering problem on the set of 3D connected components of the whole body PET tumoral segmentation mask carried out by nuclear medicine physicians. At the dynamic level, and ad-hoc algorithm is proposed in order to quantify the cancer spread time evolution which, when combined with other existing indicators, gives rise to the metabolic tumor volume-aggressiveness-spread time evolution chart, a novel tool that we claim that would prove useful in nuclear medicine and oncological clinical or research scenarios. Good performance results of the proposed methodologies both at the clinical and technological level are shown using a dataset of 48 segmented whole body FDG-PET/CT scans.  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ SDE2014b Serial 2548  
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Author Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio edit  doi
openurl 
  Title Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source Type Journal Article
  Year 2015 Publication Journal of Medical Imaging and Health Informatics Abbreviated Journal JMIHI  
  Volume (up) 5 Issue 2 Pages 192-201  
  Keywords CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY  
  Abstract Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level.  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ SED2015 Serial 2584  
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Author Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans edit  url
openurl 
  Title Improved RGB-D-T based Face Recognition Type Journal Article
  Year 2016 Publication IET Biometrics Abbreviated Journal BIO  
  Volume (up) 5 Issue 4 Pages 297 - 303  
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  Abstract Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.  
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  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ OCN2016 Serial 2854  
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Author Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari edit  doi
openurl 
  Title Dominant and Complementary Emotion Recognition from Still Images of Faces Type Journal Article
  Year 2018 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume (up) 6 Issue Pages 26391 - 26403  
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  Abstract Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.  
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  Notes HUPBA; no proj;MV;OR;MILAB Approved no  
  Call Number Admin @ si @ GLW2018 Serial 3122  
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Author Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier edit  doi
openurl 
  Title Multimodal First Impression Analysis with Deep Residual Networks Type Journal Article
  Year 2018 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume (up) 8 Issue 3 Pages 316-329  
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  Abstract People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations  
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  Notes HUPBA; no proj;MV;OR;MILAB Approved no  
  Call Number Admin @ si @ GGB2018 Serial 3210  
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