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Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a common tool for medical experts to the diagnosis of glioma. A set of multi-sequences from an MRI is selected according to the severity of the pathology. Our proposed approach aims moreto create a computer-aided system that is capable of helping morethe expert diagnose the brain gliomas. moreWe propose a supervised learning regime based on a convolutional neural network based framework and transfer learning techniques. Our research morefocuses on the performance of different pre-trained deep learning models with respect to different MRI sequences. We highlight the best combinations of such model-MRI sequence couple for our specific task of classifying healthy brain against brain with glioma. moreWe also propose to visually analyze the extracted deep features for studying the existing relation of the MRI sequences and models. This interpretability analysis gives some hints for medical expert to understand the diagnosis made by the models. Our study is based on the well-known BraTS datasets including multi-sequence images and expert diagnosis.

To study neurodegenerative diseases, longitudinal studies are carried on volunteer patients. During a time span of several months to several years, they go through regular medical visits to acquire data from different modalities, such as biological samples, cognitive tests, structural and functional imaging. These variables are heterogeneous but they all depend on the patient's health condition, meaning that there are possibly unknown relationships between all modalities. Some information may be specific to some modalities, others may be complementary, and others may be redundant. Some data may also be missing. In this work we present a neural network architecture for multimodal learning, able to use imaging and clinical data from two time points to predict the evolution of a neurodegenerative disease, and robust to missing values. Our multimodal network achieves 92.5\% accuracy and an AUC score of 0.978 over a test set of 57 subjects. We also show the superiority of the multimodal architecture, for up to 37.5\% of missing values in test set subjects' clinical measurements, compared to a model using only the clinical modality., Accepted in the 10th International Conference on Image Processing Theory, Tools and Applications (IPTA 2020)

Prediction of the cognitive evolution of a person susceptible to develop a neurodegenerative disorder is crucial to provide an appropriate treatment as soon as possible. In this paper we propose a 3D siamese network designed to extract features from whole-brain 3D MRI images. We show that it is possible to extract meaningful features using convolution layers, reducing the need of classical image processing operations such as segmentation or pre-computing features such as cortical thickness. To lead this study we used the Alzheimer's Disease Neuroimaging Initiative (ADNI), a public data base of 3D MRI brain images. A set of 247 subjects has been extracted, all of the subjects having 2 images in a range of 12 months. In order to measure the evolution of the patients states we have compared these 2 images. Our work has been inspired at the beginning by an article of Bhagwat et al. in 2018, who have proposed a siamese network to predict the status of patients but without any convolutional layers and reducing the MRI images to a vector of features extracted from predefined ROIs. We show that our network achieves an accuracy of 90% in the classification of cognitively declining VS stable patients. This result has been obtained without the help of a cognitive score and with a small number of patients comparing to the current datasets size claimed in deep learning domain.

The task of verifying the originality and authenticity of images puts numerous constraints on what tampering detection algorithms should be able to achieve. Since most images are acquired on the Internet, there is a significant probability that they have undergone transformations such as compression, noising, re-sizing and / or filtering, both before and after the possible alteration. Therefore, it is essential to improve the robustness of tampered image detection algorithms for such manipulations. As compression is the most common type of post-processing, we propose in our work a robust framework against this particular transformation. Our experiments on benchmark datasets show the contribution of our proposal for camera model identification and image tampering detection compared to recent literature approaches.

International audience; Content-based image retrieval systems are meant to retrieve the most similar images of a collection to a query image. One of the most well-known models widely applied for this task is the bag of visual words (BoVW) model. In this paper, we introduce a study of different information gain models used for the construction of a visual vocabulary. In the proposed framework, information gain models are used as a discriminative information to index image features and select the ones that have the highest information gain values. We introduce some extensions to further improve the performance of the proposed framework: mixing different vocabularies and extending the BoVW to bag of visual phrases. Exhaustive experiments show the interest of information gain models on our retrieval framework.

International audience; Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

Having effective methods to access the images with desired object is essential nowadays with the availability of huge amount of digital images. We propose a semantic higher-level visual representation which improves the traditional part-based bag-of words image representation, in two aspects. First, we propose a semantic model to generate a semantic visual words and phrases in order to bridge the semantic gab factor. Second, the approach strengthens the discrimination power of classical visual words by constructing an mid level descriptor, Semantic Visual Phrase, from frequently co-occurring Semantic Visual Words set in the same local context.

Having effective methods to access the desired images is essential nowadays with the availability of huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists in weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness.

In this paper, we present a user study on a novel video search interface based on the concept of aspect browsing. We aim to confirm whether automatically suggesting new aspects can increase the performance of an aspect-based browser. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. We use a clustering technique to identify potential aspects and use the results to propose suggestions to the user to help them in their search task. We evaluate this approach by analysing the users' perception and by exploiting the log files.

In this paper we introduce a novel interactive video retrieval approach which uses sub-needs of an information need for querying and organising the search process. The underlying assumption of this approach is that the search effectiveness will be enhanced when employed for interactive video retrieval. We explore the performance bounds of a faceted system by using the simulated user evaluation methodology on TRECVID data sets and also on the logs of a prior user experiment with the system. We discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. The facets are simulated by the use of clustering the video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness.

In this paper, we present a novel video search interface based on the concept of aspect browsing. The proposed strategy is to assist the user in exploratory video search by actively suggesting new query terms and video shots. Our approach has the potential to narrow the "Semantic Gap" issue by allowing users to explore the data collection. First, we describe a clustering technique to identify potential aspects of a search. Then, we use the results to propose suggestions to the user to help them in their search task. Finally, we analyse this approach by exploiting the log files and the feedbacks of a user study.

The recent increase of online video has challenged the research in the field of video information retrieval. Video search engines are becoming more and more interactive, helping the user to easily find what he or she is looking for. In this poster, we present a new approach of using an iterative clustering algorithm on text and visual features to simulate users creating new facets in a facet-based interface. Our experimental results prove the usefulness of such an approach.

Eye movements are certainly the most natural and repetitive movement of a human being. The most mundane activity, such as watching television or reading a newspaper, involves this automatic activity which consists of shifting our gaze from one point to another. Identification of the components of eye movements (fixations and saccades) is an essential part in the analysis of visual behavior because these types of movements provide the basic elements used by further investigations of human vision. However, many of the algorithms that detect fixations present a number of problems. In this article, we present a new fixation identification technique that is based on clustering of eye positions, using projections and projection aggregation applied to static pictures. We also present a new method that computes dispersion of eye fixations in videos considering a multiuser environment. To demonstrate the performance and usefulness of our approach we discuss our experimental work with two different applications: on fixed image and video.

Clustering algorithms for multidimensional numerical data must overcome special difficulties due to the irregularities of data distribution. We present a clustering algorithm for numerical data that combines ideas from random projection techniques and density-based clustering. The algorithm consists of two phases: the first phase that entails the use of random projections to detect clusters, and the second phase that consists of certain post-processing techniques of clusters obtained by several random projections. Experiments were performed on synthetic data consisting of randomly-generated points in Rn, synthetic images containing colored regions randomly distributed, and, finally, real images. Our results suggest the potential of our algorithm for image segmentation.

Digital images have become a powerful and effective visual communication tool for delivering messages, diffusing ideas, and proving facts. The smartphone emergence with a wide variety of brands and models facilitates the creation of new visual content and its dissemination in social networks and image sharing platforms. Related to this phenomenon and helped by the availability and ease of use of image manipulation softwares, many issues have arisen ranging from the distribution of illegal content to copyright infringement. The reliability of digital images is questioned for common or expert users such as court or police investigators. A well known phenomenon and widespread examples are the "fake news" which oftenly include malicious use of digital images.Many researchers in the field of image forensic have taken up the scientific challenges associated with image manipulation. Many methods with interesting performances have been developed based on automatic image processing and more recently the adoption of deep learning. Despite the variety of techniques offered, performance are bound to specific conditions and remains vulnerable to relatively simple malicious attacks. Indeed, the images collected on the Internet impose many constraints on algorithms questioning many existing integrity verification techniques. There are two main peculiarities to be taken into account for the detection of a falsification: one is the lack of information on pristine image acquisition, the other is the high probability of automatic transformations linked to the image-sharing platforms such as lossy compression or resizing.In this thesis, we focus on several of these image forensic challenges including camera model identification and image tampering detection. After reviewing the state of the art in the field, we propose a first data-driven method for identifying camera models. We use deep learning techniques based on convolutional neural networks (CNNs) and develop a learning strategy considering the quality of the input data versus the applied transformation. A family of CNN networks has been designed to learn the characteristics of the camera model directly from a collection of images undergoing the same transformations as those commonly used on the Internet. Our interest focused on lossy compression for our experiments, because it is the most used type of post-processing on the Internet. The proposed approach, therefore, provides a robust solution to compression for camera model identification. The performance achieved by our camera model detection approach is also used and adapted for image tampering detection and localization. The performances obtained underline the robustness of our proposals for camera model identification and image forgery detection., Les images numériques sont devenues un outil de communication visuel puissant et efficace pour transmettre des messages, diffuser des idées et prouver des faits. L’apparition du smartphone avec une grande diversité de marques et de modèles facilite la création de nouveaux contenus visuels et leur diffusion dans les réseaux sociaux et les plateformes de partage d’images. Liés à ce phénomène de création et publication d'images et aidés par la disponibilité et la facilité d’utilisation des logiciels de manipulation d’images, de nombreux problèmes sont apparus allant de la diffusion de contenu illégal à la violation du droit d’auteur. La fiabilité des images numériques est remise en cause que ce soit pour de simples utilisateurs ou pour des professionnels experts tels que les tribunaux et les enquêteurs de police. Le phénomène des « fake news » est un exemple bien connu et répandu d’utilisation malveillante d’images numériques sur les réseaux.De nombreux chercheurs du domaine de la cybersécurité des images ont relevé les défis scientifiques liés aux manipulations des images. De nombreuses méthodes aux performances intéressantes ont été développées basées sur le traitement automatique des images et plus récemment l'adoption de l'apprentissage profond. Malgré la diversité des techniques proposées, certaines ne fonctionnent que pour certaines conditions spécifiques et restent vulnérables à des attaques malveillantes relativement simples. En effet, les images collectées sur Internet imposent de nombreuses contraintes aux algorithmes remettant en question de nombreuses techniques de vérification d’intégrité existantes. Il existe deux particularités principales à prendre en compte pour la détection d'une falsification : l’une est le manque d'informations sur l'acquisition de l'image d'origine, l'autre est la forte probabilité de transformations automatiques liées au partage de l'image telles que la compression avec pertes ou le redimensionnement.Dans cette thèse, nous sommes confrontés à plusieurs de ces défis liés à la cybersécurité des images notamment l’identification de modèles de caméra et la détection de falsification d’images. Après avoir passé en revue l'état de l'art du domaine, nous proposons une première méthode basée sur les données pour l’identification de modèles de caméra. Nous utilisons les techniques d’apprentissage profond basées sur les réseaux de neurones convolutifs (CNN) et développons une stratégie d’apprentissage prenant en compte la qualité des données d’entrée par rapport à la transformation appliquée. Une famille de réseaux CNN a été conçue pour apprendre les caractéristiques du modèle de caméra directement à partir d’une collection d’images subissant les mêmes transformations que celles couramment utilisées sur Internet. Notre intérêt s'est porté sur la compression avec pertes pour nos expérimentations, car c’est le type de post-traitement le plus utilisé sur Internet. L’approche proposée fournit donc une solution robuste face à la compression pour l’identification de modèles de caméra. Les performances obtenues par notre approche de détection de modèles de caméra sont également utilisées et adaptées pour la détection et la localisation de falsification d’images. Les performances obtenues soulignent la robustesse de nos propositions pour la classification de modèles de caméra et la détection de falsification d'images.