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| Author | 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 | ||||
| Title | Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Medical Imaging | Abbreviated Journal | TMI |
| Volume | 36 | Issue | 6 | Pages | 1231 - 1249 |
| Keywords | Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework | ||||
| 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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| Notes | MV; 600.096; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ BTS2017 | Serial  |
2949 | ||
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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 | ||||
| Title | Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis | Type | Conference Article | ||
| Year | 2017 | Publication | 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery | Abbreviated Journal | |
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| Address | Barcelona; Spain; June 2017 | ||||
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| Area | Expedition | Conference | CARS | ||
| Notes | MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ ABS2017 | Serial |
2947 | ||
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| Author | Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure | ||||
| Title | Slanted Stixels: Representing San Francisco's Steepest Streets} | Type | Conference Article | ||
| Year | 2017 | Publication | 28th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. | ||||
| Address | London; uk; September 2017 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ HSE2017a | Serial |
2945 | ||
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| Author | Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title | Towards a Videobronchoscopy Localization System from Airway Centre Tracking | Type | Conference Article | ||
| Year | 2017 | Publication | 12th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 352-359 | ||
| Keywords | Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation | ||||
| 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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| Address | Porto; Portugal; February 2017 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SEB2017 | Serial |
2943 | ||
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| Author | David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville | ||||
| Title | A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of Healthcare Engineering | Abbreviated Journal | JHCE |
| Volume | Issue | Pages | 2040-2295 | ||
| Keywords | Colonoscopy images; Deep Learning; Semantic Segmentation | ||||
| Abstract | 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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| Notes | ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
| Call Number | VBS2017b | Serial |
2940 | ||
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| Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva | ||||
| Title | VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering | Type | Conference Article | ||
| Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks | ||||
| Abstract | 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. | ||||
| Address | Faro; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BPC2017 | Serial |
2939 | ||
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| Author | Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez | ||||
| Title | Computer Vision in Vehicle Technology: Land, Sea & Air | Type | Book Whole | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 161-163 | ||
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| Abstract | Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition. |
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| Publisher | John Wiley & Sons, Ltd | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-1-118-86807-2 | Medium | ||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LIP2017a | Serial |
2937 | ||
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| Author | Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate | ||||
| Title | Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study | Type | Conference Article | ||
| Year | 2017 | Publication | 11th European CytoGenesis Conference | Abbreviated Journal | |
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| Address | Florencia; Italia; July 2017 | ||||
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| Area | Expedition | Conference | ECA | ||
| Notes | IAM; 600.096; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SBG2017a | Serial |
2936 | ||
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| Author | H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil | ||||
| Title | Medial structure generation for registration of anatomical structures | Type | Book Chapter | ||
| Year | 2017 | Publication | Skeletonization, Theory, Methods and Applications | Abbreviated Journal | |
| Volume | 11 | Issue | Pages | ||
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| Notes | IAM; 600.096; 600.075; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ MFV2017a | Serial |
2935 | ||
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| Author | Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven | ||||
| Title | End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks | Type | Miscellaneous | ||
| Year | 2017 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | 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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| Notes | HuPBA; ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ GGM2017 | Serial |
2932 | ||
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| Author | Karim Lekadir; Alfiia Galimzianova; Angels Betriu; Maria del Mar Vila; Laura Igual; Daniel L. Rubin; Elvira Fernandez-Giraldez; Petia Radeva; Sandy Napel | ||||
| Title | A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Journal Biomedical and Health Informatics | Abbreviated Journal | J-BHI |
| Volume | 21 | Issue | 1 | Pages | 48-55 |
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| Abstract | Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LGB2017 | Serial |
2931 | ||
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| Author | Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel | ||||
| Title | Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition | Type | Book Chapter | ||
| Year | 2017 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 9657 | Issue | Pages | ||
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| Publisher | Springer | Place of Publication | Editor | B. Lamiroy; R Dueire Lins | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ JLR2017 | Serial |
2928 | ||
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| Author | Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca | ||||
| Title | Regularizing CNNs with Locally Constrained Decorrelations | Type | Conference Article | ||
| Year | 2017 | Publication | 5th International Conference on Learning Representations | Abbreviated Journal | |
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| Address | Toulon; France; April 2017 | ||||
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| Area | Expedition | Conference | ICLR | ||
| Notes | ISE; 602.143; 600.119; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ RGC2017 | Serial |
2927 | ||
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| Author | Pau Rodriguez; Guillem Cucurull; Jordi Gonzalez; Josep M. Gonfaus; Kamal Nasrollahi; Thomas B. Moeslund; Xavier Roca | ||||
| Title | Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | Cyber |
| Volume | Issue | Pages | 1-11 | ||
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| Abstract | Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database. | ||||
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| Notes | ISE; 600.119; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ RCG2017a | Serial |
2926 | ||
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| Author | Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari | ||||
| Title | Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions. | ||||
| Address | Washington; DC; USA; May 2017 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LRL2017 | Serial |
2925 | ||
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