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Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
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A computational framework for cancer response assessment based on oncological PET-CT scans |
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Journal Article |
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2014 |
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Computers in Biology and Medicine |
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CBM |
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55 |
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92–99 |
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Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis |
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In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks. |
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HuPBA;MILAB |
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no |
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Admin @ si @ SED2014 |
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2606 |
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Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
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Title |
Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification |
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Journal Article |
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Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2015 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
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18 |
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2 |
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247-261 |
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Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification |
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In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. |
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Springer-Verlag |
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1433-7541 |
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HuPBA;MILAB |
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Admin @ si @ PEP2013 |
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2251 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Combining Local and Global Learners in the Pairwise Multiclass Classification |
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2015 |
Publication |
Pattern Analysis and Applications |
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PAA |
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18 |
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4 |
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845-860 |
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Multiclass classification; Pairwise approach; One-versus-one |
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Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. |
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Springer London |
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1433-7541 |
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HuPBA;MILAB |
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no |
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Admin @ si @ BGE2014 |
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2441 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
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Title |
Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source |
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Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2015 |
Publication |
Journal of Medical Imaging and Health Informatics |
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JMIHI |
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5 |
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2 |
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192-201 |
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CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY |
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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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HuPBA;MILAB |
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Admin @ si @ SED2015 |
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2584 |
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Permanent link to this record |
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Author |
Alvaro Cepero; Albert Clapes; Sergio Escalera |
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Title |
Automatic non-verbal communication skills analysis: a quantitative evaluation |
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Journal Article |
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Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2015 |
Publication |
AI Communications |
Abbreviated Journal |
AIC |
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28 |
Issue |
1 |
Pages |
87-101 |
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Keywords |
Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
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Abstract |
The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
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0921-7126 |
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HUPBA;MILAB |
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Admin @ si @ CCE2015 |
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2549 |
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Permanent link to this record |