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	Author	Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil
	Title	Selection of Radiomics Features based on their Reproducibility			Type	Conference Article
	Year	2019	Publication	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society	Abbreviated Journal
	Volume		Issue		Pages	403-408
	Keywords
	Abstract	Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.
	Address	Berlin; Alemanya; July 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	EMBC
	Notes	IAM; 600.139; 600.145			Approved	no
	Call Number	Admin @ si @ LTS2019			Serial	3358
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	Author	Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez
	Title	Data Augmentation from Sketch			Type	Conference Article
	Year	2019	Publication	International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging	Abbreviated Journal
	Volume	11840	Issue		Pages	155-162
	Keywords	Data augmentation; cycleGANs; Multi-objective optimization
	Abstract	State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content. We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.
	Address	Shenzhen; China; October 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CLIP
	Notes	IAM; 600.145; 601.337; 600.139; 600.145			Approved	no
	Call Number	Admin @ si @ GES2019			Serial	3359
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	Author	Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil
	Title	Back to Front Architecture for Diagnosis as a Service			Type	Conference Article
	Year	2018	Publication	20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing	Abbreviated Journal
	Volume		Issue		Pages	343-346
	Keywords
	Abstract	Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction.
	Address	Timisoara; Rumania; September 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	SYNASC
	Notes	IAM; 600.145			Approved	no
	Call Number	Admin @ si @ SVA2018			Serial	3360
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	Author	Debora Gil; Antoni Rosell
	Title	Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress?			Type	Abstract
	Year	2019	Publication	World Lung Cancer Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Invited speaker
	Address	Barcelona; September 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IASLC WCLC
	Notes	IAM; 600.139; 600.145			Approved	no
	Call Number	Admin @ si @ GiR2019			Serial	3361
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	Author	Rada Deeb; Joost Van de Weijer; Damien Muselet; Mathieu Hebert; Alain Tremeau
	Title	Deep spectral reflectance and illuminant estimation from self-interreflections			Type	Journal Article
	Year	2019	Publication	Journal of the Optical Society of America A	Abbreviated Journal	JOSA A
	Volume	31	Issue	1	Pages	105-114
	Keywords
	Abstract	In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ DWM2019			Serial	3362
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	Author	Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz
	Title	SDIT: Scalable and Diverse Cross-domain Image Translation			Type	Conference Article
	Year	2019	Publication	27th ACM International Conference on Multimedia	Abbreviated Journal
	Volume		Issue		Pages	1267–1276
	Keywords
	Abstract	Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.
	Address	Nice; Francia; October 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ACM-MM
	Notes	LAMP; 600.106; 600.109; 600.141; 600.120			Approved	no
	Call Number	Admin @ si @ WGW2019			Serial	3363
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	Author	Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit
	Title	Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding			Type	Journal Article
	Year	2021	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	149	Issue		Pages	164-171
	Keywords
	Abstract	Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ DGV2021			Serial	3364
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	Author	Mohammed Al Rawi; Ernest Valveny
	Title	Compact and Efficient Multitask Learning in Vision, Language and Speech			Type	Conference Article
	Year	2019	Publication	IEEE International Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages	2933-2942
	Keywords
	Abstract	Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains.
	Address	Seul; Korea; October 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICCVW
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ RaV2019			Serial	3365
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	Author	Eduardo Aguilar; Petia Radeva
	Title	Class-Conditional Data Augmentation Applied to Image Classification			Type	Conference Article
	Year	2019	Publication	18th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	11679	Issue		Pages	182-192
	Keywords	CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition
	Abstract	Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty.
	Address	Salermo; Italy; September 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CAIP
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ AgR2019			Serial	3366
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	Author	Estefania Talavera; Nicolai Petkov; Petia Radeva
	Title	Unsupervised Routine Discovery in Egocentric Photo-Streams			Type	Conference Article
	Year	2019	Publication	18th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	11678	Issue		Pages	576-588
	Keywords	Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis
	Abstract	The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people.
	Address	Salermo; Italy; September 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CAIP
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ TPR2019a			Serial	3367
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	Author	Md.Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig
	Title	Food Places Classification in Egocentric Images Using Siamese Neural Networks			Type	Conference Article
	Year	2019	Publication	22nd International Conference of the Catalan Association of Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages	145-151
	Keywords
	Abstract	Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.
	Address	Illes Balears; October 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CCIA
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ SBR2019			Serial	3368
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	Author	Eduardo Aguilar; Petia Radeva
	Title	Food Recognition by Integrating Local and Flat Classifiers			Type	Conference Article
	Year	2019	Publication	9th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	11867	Issue		Pages	65-74
	Keywords
	Abstract	The recognition of food image is an interesting research topic, in which its applicability in the creation of nutritional diaries stands out with the aim of improving the quality of life of people with a chronic disease (e.g. diabetes, heart disease) or prone to acquire it (e.g. people with overweight or obese). For a food recognition system to be useful in real applications, it is necessary to recognize a huge number of different foods. We argue that for very large scale classification, a traditional flat classifier is not enough to acquire an acceptable result. To address this, we propose a method that performs prediction with local classifiers, based on a class hierarchy, or with flat classifier. We decide which approach to use, depending on the analysis of both the Epistemic Uncertainty obtained for the image in the children classifiers and the prediction of the parent classifier. When our criterion is met, the final prediction is obtained with the respective local classifier; otherwise, with the flat classifier. From the results, we can see that the proposed method improves the classification performance compared to the use of a single flat classifier.
	Address	Madrid; July 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ AgR2019b			Serial	3369
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	Author	Emanuel Sanchez Aimar; Petia Radeva; Mariella Dimiccoli
	Title	Social Relation Recognition in Egocentric Photostreams			Type	Conference Article
	Year	2019	Publication	26th International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages	3227-3231
	Keywords
	Abstract	This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.
	Address	Taipei; Taiwan; September 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICIP
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ SRD2019			Serial	3370
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	Author	C. Alejandro Parraga; Xavier Otazu; Arash Akbarinia
	Title	Modelling symmetry perception with banks of quadrature convolutional Gabor kernels			Type	Conference Article
	Year	2019	Publication	42nd edition of the European Conference on Visual Perception	Abbreviated Journal
	Volume		Issue		Pages	224-224
	Keywords
	Abstract	Mirror symmetry is a property most likely to be encountered in animals than in medium scale vegetation or inanimate objects in the natural world. This might be the reason why the human visual system has evolved to detect it quickly and robustly. Indeed, the perception of symmetry assists higher-level visual processing that are crucial for survival such as target recognition and identification irrespective of position and location. Although the task of detecting symmetrical objects seems effortless to us, it is very challenging for computers (to the extent that it has been proposed as a robust “captcha” by Funk & Liu in 2016). Indeed, the exact mechanism of symmetry detection in primates is not well understood: fMRI studies have shown that symmetrical shapes activate specific higher-level areas of the visual cortex (Sasaki et al.; 2005) and similarly, a large body of psychophysical experiments suggest that the symmetry perception is critically influenced by low-level mechanisms (Treder; 2010). In this work we attempt to find plausible low-level mechanisms that might form the basis for symmetry perception. Our simple model is made from banks of (i) odd-symmetric Gabors (resembling edge-detecting V1 neurons); and (ii) banks of larger odd- and even-symmetric Gabors (resembling higher visual cortex neurons), that pool signals from the 'edge image'. As reported previously (Akbarinia et al, ECVP2017), the convolution of the symmetrical lines with the two Gabor kernels of alternative phase produces a minimum in one and a maximum in the other (Osorio; 1996), and the rectification and combination of these signals create lines which hint of mirror symmetry in natural images. We improved the algorithm by combining these signals across several spatial scales. Our preliminary results suggest that such multiscale combination of convolutional operations might form the basis for much of the operation of the HVS in terms of symmetry detection and representation.
	Address	Leuven; Belgium; August 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ECVP
	Notes	NEUROBIT; 600.128			Approved	no
	Call Number	Admin @ si @ POA2019			Serial	3371
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	Author	David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Xose M. Pardo
	Title	SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention Modeling			Type	Conference Article
	Year	2019	Publication	18th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	8788-8797
	Keywords
	Abstract	A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor popout type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-ofthe-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets.
	Address	Seul; Corea; October 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICCV
	Notes	NEUROBIT; 600.128			Approved	no
	Call Number	Admin @ si @ BFO2019b			Serial	3372
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