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Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven |
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Title |
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks |
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Miscellaneous |
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2017 |
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Arxiv |
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arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them. |
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HuPBA; ISE; 600.098; 600.119 |
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no |
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Admin @ si @ GGM2017 |
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2932 |
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Author |
H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil |
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Title |
Medial structure generation for registration of anatomical structures |
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Book Chapter |
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Year |
2017 |
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Skeletonization, Theory, Methods and Applications |
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11 |
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IAM; 600.096; 600.075; 600.145 |
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Admin @ si @ MFV2017a |
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2935 |
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Author |
Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate |
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Title |
Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study |
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Conference Article |
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2017 |
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11th European CytoGenesis Conference |
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Florencia; Italia; July 2017 |
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ECA |
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IAM; 600.096; 600.145 |
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Admin @ si @ SBG2017a |
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2936 |
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Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva |
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Title |
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering |
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Conference Article |
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2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
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Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks |
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In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. |
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Faro; Portugal; June 2017 |
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IbPRIA |
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MILAB; no proj |
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no |
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Admin @ si @ BPC2017 |
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2939 |
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Author |
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
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Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
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Journal Article |
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Year |
2017 |
Publication |
Journal of Healthcare Engineering |
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JHCE |
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2040-2295 |
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Colonoscopy images; Deep Learning; Semantic Segmentation |
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Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. |
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ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
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no |
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Call Number |
VBS2017b |
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2940 |
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Author |
Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Towards a Videobronchoscopy Localization System from Airway Centre Tracking |
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Conference Article |
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Year |
2017 |
Publication |
12th International Conference on Computer Vision Theory and Applications |
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352-359 |
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Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation |
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Abstract |
Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a
feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations. |
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Porto; Portugal; February 2017 |
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VISAPP |
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IAM; 600.096; 600.075; 600.145 |
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no |
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Admin @ si @ SEB2017 |
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2943 |
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Author |
Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell |
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Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
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Journal Article |
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2017 |
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European Respiratory Journal |
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ERJ |
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IAM |
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no |
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Admin @ si @ DGC2017b |
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3632 |
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Author |
Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Maroua Hammami; Gloria Fernandez Esparrach; Xavier Dray; Olivier Romain; F. Javier Sanchez; Aymeric Histace |
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Title |
Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis |
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Conference Article |
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2017 |
Publication |
31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
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Barcelona; Spain; June 2017 |
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CARS |
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MV; no menciona |
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no |
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Admin @ si @ ABS2017 |
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2947 |
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Jorge Bernal; Nima Tajkbaksh; F. Javier Sanchez; Bogdan J. Matuszewski; Hao Chen; Lequan Yu; Quentin Angermann; Olivier Romain; Bjorn Rustad; Ilangko Balasingham; Konstantin Pogorelov; Sungbin Choi; Quentin Debard; Lena Maier Hein; Stefanie Speidel; Danail Stoyanov; Patrick Brandao; Henry Cordova; Cristina Sanchez Montes; Suryakanth R. Gurudu; Gloria Fernandez Esparrach; Xavier Dray; Jianming Liang; Aymeric Histace |
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Title |
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge |
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Journal Article |
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Year |
2017 |
Publication |
IEEE Transactions on Medical Imaging |
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TMI |
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36 |
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6 |
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1231 - 1249 |
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Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework |
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Abstract |
Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack
of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance. |
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MV; 600.096; 600.075 |
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no |
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Admin @ si @ BTS2017 |
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2949 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
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Title |
Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs |
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Conference Article |
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Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
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Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines |
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We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. |
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Faro; Portugal; June 2017 |
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IbPRIA |
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DAG; 600.097; 600.121 |
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no |
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Admin @ si @ JRL2017a |
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2953 |
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Author |
Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas |
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Title |
myStone: A system for automatic kidney stone classification |
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Journal Article |
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Year |
2017 |
Publication |
Expert Systems with Applications |
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ESA |
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89 |
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41-51 |
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Kidney stone; Optical device; Computer vision; Image classification |
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Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. |
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ADAS; MSIAU; 603.046; 600.122; 600.118 |
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Admin @ si @ SLB2017 |
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3026 |
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Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell |
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Title |
Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
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Conference Article |
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2017 |
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6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging |
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10550 |
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151-159 |
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Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Quebec; Canada; September 2017 |
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CLIP |
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IAM; 600.096; 600.075; 600.145 |
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no |
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Admin @ si @ GRM2017 |
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2957 |
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Author |
Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate |
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Title |
Is there a pattern of Chromosome territoriality along mice spermatogenesis? |
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Conference Article |
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2017 |
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3rd Spanish MeioNet Meeting Abstract Book |
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55-56 |
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Miraflores de la Sierra; Madrid; June 2017 |
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MEIONET |
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IAM; 600.096; 600.145 |
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Admin @ si @ |
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2958 |
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Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate |
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Unraveling the enigmas of chromosome territoriality during spermatogenesis |
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Conference Article |
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2017 |
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IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia |
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UAB; Barcelona; June 2017 |
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IAM; 600.145 |
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Admin @ si @ SBG2017b |
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2959 |
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Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez |
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Age and gender recognition in the wild with deep attention |
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2017 |
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Pattern Recognition |
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72 |
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563-571 |
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Age recognition; Gender recognition; Deep neural networks; Attention mechanisms |
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Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy. |
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ISE; 600.098; 602.133; 600.119 |
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Admin @ si @ RCG2017b |
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2962 |
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