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| Author | Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta | ||||
| Title | Diaphragm border detection in coronary X-ray angiographies: New method and applications | Type | Journal Article | ||
| Year | 2014 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 38 | Issue | 4 | Pages | 296-305 |
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| Abstract | X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation. | ||||
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MILAB; LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ PCR2014 | Serial | 2468 | ||
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| Author | Adriana Romero; Petia Radeva; Carlo Gatta | ||||
| Title | No more meta-parameter tuning in unsupervised sparse feature learning | Type | Miscellaneous | ||
| Year | 2014 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
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MILAB; LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ RRG2014 | Serial | 2471 | ||
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| Author | Adriana Romero; Carlo Gatta; Gustavo Camps-Valls | ||||
| Title | Unsupervised Deep Feature Extraction Of Hyperspectral Images | Type | Conference Article | ||
| Year | 2014 | Publication | 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification | ||||
| Abstract | This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. | ||||
| Address | Lausanne; Switzerland; June 2014 | ||||
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| Area | Expedition | Conference | WHISPERS | ||
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MILAB; LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ RGC2014 | Serial | 2513 | ||
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| Author | Simone Balocco; Carlo Gatta; Francesco Ciompi; A. Wahle; Petia Radeva; S. Carlier; G. Unal; E. Sanidas; J. Mauri; X. Carillo; T. Kovarnik; C. Wang; H. Chen; T. P. Exarchos; D. I. Fotiadis; F. Destrempes; G. Cloutier; Oriol Pujol; Marina Alberti; E. G. Mendizabal-Ruiz; M. Rivera; T. Aksoy; R. W. Downe; I. A. Kakadiaris | ||||
| Title | Standardized evaluation methodology and reference database for evaluating IVUS image segmentation | Type | Journal Article | ||
| Year | 2014 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 38 | Issue | 2 | Pages | 70-90 |
| Keywords | IVUS (intravascular ultrasound); Evaluation framework; Algorithm comparison; Image segmentation | ||||
| Abstract | This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated.
We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved. |
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MILAB; LAMP; HuPBA; 600.046; 600.063; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ BGC2013 | Serial | 2314 | ||
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| Author | Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria | ||||
| Title | Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Information Technology in Biomedicine | Abbreviated Journal | TITB |
| Volume | 16 | Issue | 6 | Pages | 1341-1352 |
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| Abstract | Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. | ||||
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| ISSN | 1089-7771 | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | ||
| Notes |
MILAB; MV; OR;SIAI | Approved | no | ||
| Call Number | Admin @ si @ SDV2012 | Serial | 2124 | ||
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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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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LGB2017 | Serial | 2931 | ||
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| Author | I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi | ||||
| Title | Learning quantification from images: A structured neural architecture | Type | Journal Article | ||
| Year | 2018 | Publication | Natural Language Engineering | Abbreviated Journal | NLE |
| Volume | 24 | Issue | 3 | Pages | 363-392 |
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| Abstract | Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area. | ||||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SPH2018 | Serial | 3021 | ||
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| Author | Alejandro Cartas; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Batch-based activity recognition from egocentric photo-streams | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International workshop on Egocentric Perception, Interaction and Computing | Abbreviated Journal | |
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| Abstract | Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries. | ||||
| Address | Venice; Italy; October 2017; | ||||
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| Area | Expedition | Conference | ICCV - EPIC | ||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ CDR2017 | Serial | 3023 | ||
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| Author | Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva | ||||
| Title | All the people around me: face clustering in egocentric photo streams | Type | Conference Article | ||
| Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams | ||||
| Abstract | arxiv1703.01790
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose. |
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| Address | Beijing; China; September 2017 | ||||
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| Area | Expedition | Conference | ICIP | ||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EDR2017 | Serial | 3025 | ||
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| Author | Mireia Forns-Nadal; Federico Sem; Anna Mane; Laura Igual; Dani Guinart; Oscar Vilarroya | ||||
| Title | Increased Nucleus Accumbens Volume in First-Episode Psychosis | Type | Journal Article | ||
| Year | 2017 | Publication | Psychiatry Research-Neuroimaging | Abbreviated Journal | PRN |
| Volume | 263 | Issue | Pages | 57-60 | |
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| Abstract | Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant. | ||||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSM2017 | Serial | 3028 | ||
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| Author | Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
| Title | CuisineNet: Food Attributes Classification using Multi-scale Convolution Network | Type | Conference Article | ||
| Year | 2018 | Publication | 21st International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | 365-372 | ||
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| Abstract | Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. | ||||
| Address | Roses; catalonia; October 2018 | ||||
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| Area | Expedition | Conference | CCIA | ||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SJR2018 | Serial | 3113 | ||
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| Author | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
| Title | MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams | Type | Conference Article | ||
| Year | 2018 | Publication | European Conference on Computer Vision workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 423-433 | ||
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| Abstract | First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LCNS | ||
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| Area | Expedition | Conference | ECCVW | ||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SRR2018b | Serial | 3185 | ||
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| Author | Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva | ||||
| Title | CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation | Type | Journal Article | ||
| Year | 2021 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
| Volume | 136 | Issue | Pages | 104689 | |
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| Abstract | The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. | ||||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ PDR2021 | Serial | 3635 | ||
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| Author | Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas | ||||
| Title | Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets | Type | Conference Article | ||
| Year | 2018 | Publication | International Workshop on Artificial Intelligence and Pattern Recognition | Abbreviated Journal | |
| Volume | 11047 | Issue | Pages | 34-42 | |
| Keywords | Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis | ||||
| Abstract | The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. | ||||
| Address | Cuba; September 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IWAIPR | ||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BGÑ2018 | Serial | 3237 | ||
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| Author | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
| Title | Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume | 7 | Issue | Pages | 39069-39082 | |
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| Abstract | Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called “EgoFoodPlaces” that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the “EgoFoodPlaces” dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3. | ||||
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MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SRA2019 | Serial | 3296 | ||
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