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Статті в журналах з теми "Segmentation par apprentissage profond"
Fillières-Riveau, Gauthier, Jean-Marie Favreau, Vincent Barra, and Guillaume Touya. "Génération de cartes tactiles photoréalistes pour personnes déficientes visuelles par apprentissage profond." Revue Internationale de Géomatique 30, no. 1-2 (January 2020): 105–26. http://dx.doi.org/10.3166/rig.2020.00104.
Повний текст джерелаPouliquen, Geoffroy, and Catherine Oppenheim. "Débruitage par apprentissage profond: impact sur les biomarqueurs quantitatifs des tumeurs cérébrales." Journal of Neuroradiology 49, no. 2 (March 2022): 136. http://dx.doi.org/10.1016/j.neurad.2022.01.040.
Повний текст джерелаCaccamo, Emmanuelle, and Fabien Richert. "Les procédés algorithmiques au prisme des approches sémiotiques." Cygne noir, no. 7 (June 1, 2022): 1–16. http://dx.doi.org/10.7202/1089327ar.
Повний текст джерелаChoplin, Arnaud, and Julie Laporte. "Comparaison de deux stratégies pédagogiques dans l’apprentissage du toucher thérapeutique." Revue des sciences de l’éducation 42, no. 3 (June 7, 2017): 187–210. http://dx.doi.org/10.7202/1040089ar.
Повний текст джерелаSantos, Sheila Cristina dos, and Ronaldo Lima. "Faux-amis dans les activites de traduction français-portugais." Revista Letras Raras 4, no. 1 (August 31, 2015): 160–75. http://dx.doi.org/10.35572/rlr.v4i1.400.
Повний текст джерелаBaudouin, Maxime. "Détection d'anévrisme intracrânien par apprentissage profond sur l'irm tof à l'aide d'un u-net régularisé à deux niveaux." Journal of Neuroradiology 50, no. 2 (March 2023): 187–89. http://dx.doi.org/10.1016/j.neurad.2023.01.129.
Повний текст джерелаPrakash, Prem, Marc Sebban, Amaury Habrard, Jean-Claude Barthelemy, Frédéric Roche, and Vincent Pichot. "Détection automatique des apnées du sommeil sur l’ECG nocturne par un apprentissage profond en réseau de neurones récurrents (RNN)." Médecine du Sommeil 18, no. 1 (March 2021): 43–44. http://dx.doi.org/10.1016/j.msom.2020.11.077.
Повний текст джерелаBrou Boni, K. N. D., A. Wagner, L. Vanquin, J. Klein, N. Reynaert, and D. Pasquier. "Génération de tomodensitométrie synthétique par apprentissage profond pour la radiothérapie du cancer de la prostate basée sur l’IRM seule." Cancer/Radiothérapie 23, no. 6-7 (October 2019): 797. http://dx.doi.org/10.1016/j.canrad.2019.07.022.
Повний текст джерелаIsaac, C., D. Fouques, S. Braha Zeitoun, and D. Januel. "La remédiation cognitive dans le trouble bipolaire : une étude de cas." European Psychiatry 28, S2 (November 2013): 108–9. http://dx.doi.org/10.1016/j.eurpsy.2013.09.289.
Повний текст джерелаMille, C. "Le désir mimétique chez les personnes Asperger." European Psychiatry 30, S2 (November 2015): S91—S92. http://dx.doi.org/10.1016/j.eurpsy.2015.09.393.
Повний текст джерелаДисертації з теми "Segmentation par apprentissage profond"
Bertrand, Hadrien. "Optimisation d'hyper-paramètres en apprentissage profond et apprentissage par transfert : applications en imagerie médicale." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT001/document.
Повний текст джерелаIn the last few years, deep learning has changed irrevocably the field of computer vision. Faster, giving better results, and requiring a lower degree of expertise to use than traditional computer vision methods, deep learning has become ubiquitous in every imaging application. This includes medical imaging applications. At the beginning of this thesis, there was still a strong lack of tools and understanding of how to build efficient neural networks for specific tasks. Thus this thesis first focused on the topic of hyper-parameter optimization for deep neural networks, i.e. methods for automatically finding efficient neural networks on specific tasks. The thesis includes a comparison of different methods, a performance improvement of one of these methods, Bayesian optimization, and the proposal of a new method of hyper-parameter optimization by combining two existing methods: Bayesian optimization and Hyperband.From there, we used these methods for medical imaging applications such as the classification of field-of-view in MRI, and the segmentation of the kidney in 3D ultrasound images across two populations of patients. This last task required the development of a new transfer learning method based on the modification of the source network by adding new geometric and intensity transformation layers.Finally this thesis loops back to older computer vision methods, and we propose a new segmentation algorithm combining template deformation and deep learning. We show how to use a neural network to predict global and local transformations without requiring the ground-truth of these transformations. The method is validated on the task of kidney segmentation in 3D US images
Ganaye, Pierre-Antoine. "A priori et apprentissage profond pour la segmentation en imagerie cérébrale." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI100.
Повний текст джерелаMedical imaging is a vast field guided by advances in instrumentation, acquisition techniques and image processing. Advances in these major disciplines all contribute to the improvement of the understanding of both physiological and pathological phenomena. In parallel, access to broader imaging databases, combined with the development of computing power, has fostered the development of machine learning methodologies for automatic image processing, including approaches based on deep neural networks. Among the applications where deep neural networks provide solutions, we find image segmentation, which consists in locating and delimiting in an image regions with specific properties that will be associated with the same structure. Despite many recent studies in deep learning based segmentation, learning the parameters of a neural network is still guided by quantitative performance measures that do not include high-level knowledge of anatomy. The objective of this thesis is to develop methods to integrate a priori into deep neural networks, targeting the segmentation of brain structures in MRI imaging. Our first contribution proposes a strategy for integrating the spatial position of the patch to be classified, to improve the discriminating power of the segmentation model. This first work considerably corrects segmentation errors that are far away from the anatomical reality, also improving the overall quality of the results. Our second contribution focuses on a methodology to constrain adjacency relationships between anatomical structures, directly while learning network parameters, in order to reinforce the realism of the produced segmentations. Our experiments conclude that the proposed constraint corrects non-admitted adjacencies, thus improving the anatomical consistency of the segmentations produced by the neural network
Zheng, Qiao. "Apprentissage profond pour la segmentation robuste et l’analyse explicable des images cardiaques volumiques et dynamiques." Thesis, Université Côte d'Azur (ComUE), 2019. http://www.theses.fr/2019AZUR4013.
Повний текст джерелаCardiac MRI is widely used by cardiologists as it allows extracting rich information from images. However, if done manually, the information extraction process is tedious and time-consuming. Given the advance of artificial intelligence, I develop deep learning methods to address the automation of several essential tasks on cardiac MRI analysis. First, I propose a method based on convolutional neural networks to perform cardiac segmentation on short axis MRI image stacks. In this method, since the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice, 3D-consistency and robustness is explicitly enforced. Second, I develop a method to classify cardiac pathologies, with a novel deep learning approach to extract image-derived features to characterize the shape and motion of the heart. In particular, the classification model is explainable, simple and flexible. Last but not least, the same feature extraction method is applied to an exceptionally large dataset (UK Biobank). Unsupervised cluster analysis is then performed on the extracted features in search of their further relation with cardiac pathology characterization. To conclude, I discuss several possible extensions of my research
Mlynarski, Pawel. "Apprentissage profond pour la segmentation des tumeurs cérébrales et des organes à risque en radiothérapie." Thesis, Université Côte d'Azur (ComUE), 2019. http://www.theses.fr/2019AZUR4084.
Повний текст джерелаMedical images play an important role in cancer diagnosis and treatment. Oncologists analyze images to determine the different characteristics of the cancer, to plan the therapy and to observe the evolution of the disease. The objective of this thesis is to propose efficient methods for automatic segmentation of brain tumors and organs at risk in the context of radiotherapy planning, using Magnetic Resonance (MR) images. First, we focus on segmentation of brain tumors using Convolutional Neural Networks (CNN) trained on MRIs manually segmented by experts. We propose a segmentation model having a large 3D receptive field while being efficient in terms of computational complexity, based on combination of 2D and 3D CNNs. We also address problems related to the joint use of several MRI sequences (T1, T2, FLAIR). Second, we introduce a segmentation model which is trained using weakly-annotated images in addition to fully-annotated images (with voxelwise labels), which are usually available in very limited quantities due to their cost. We show that this mixed level of supervision considerably improves the segmentation accuracy when the number of fully-annotated images is limited.\\ Finally, we propose a methodology for an anatomy-consistent segmentation of organs at risk in the context of radiotherapy of brain tumors. The segmentations produced by our system on a set of MRIs acquired in the Centre Antoine Lacassagne (Nice, France) are evaluated by an experienced radiotherapist
Zotti, Clément. "Réseaux de neurones à convolutions pour la segmentation multi structures d'images par résonance magnétique cardiaque." Mémoire, Université de Sherbrooke, 2018. http://hdl.handle.net/11143/11817.
Повний текст джерелаLuc, Pauline. "Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM024/document.
Повний текст джерелаPredictive models of the environment hold promise for allowing the transfer of recent reinforcement learning successes to many real-world contexts, by decreasing the number of interactions needed with the real world.Video prediction has been studied in recent years as a particular case of such predictive models, with broad applications in robotics and navigation systems.While RGB frames are easy to acquire and hold a lot of information, they are extremely challenging to predict, and cannot be directly interpreted by downstream applications.Here we introduce the novel tasks of predicting semantic and instance segmentation of future frames.The abstract feature spaces we consider are better suited for recursive prediction and allow us to develop models which convincingly predict segmentations up to half a second into the future.Predictions are more easily interpretable by downstream algorithms and remain rich, spatially detailed and easy to obtain, relying on state-of-the-art segmentation methods.We first focus on the task of semantic segmentation, for which we propose a discriminative approach based on adversarial training.Then, we introduce the novel task of predicting future semantic segmentation, and develop an autoregressive convolutional neural network to address it.Finally, we extend our method to the more challenging problem of predicting future instance segmentation, which additionally segments out individual objects.To deal with a varying number of output labels per image, we develop a predictive model in the space of high-level convolutional image features of the Mask R-CNN instance segmentation model.We are able to produce visually pleasing segmentations at a high resolution for complex scenes involving a large number of instances, and with convincing accuracy up to half a second ahead
Guerry, Joris. "Reconnaissance visuelle robuste par réseaux de neurones dans des scénarios d'exploration robotique. Détecte-moi si tu peux !" Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX080/document.
Повний текст джерелаThe main objective of this thesis is visual recognition for a mobile robot in difficult conditions. We are particularly interested in neural networks which present today the best performances in computer vision. We studied the concept of method selection for the classification of 2D images by using a neural network selector to choose the best available classifier given the observed situation. This strategy works when data can be easily partitioned with respect to available classifiers, which is the case when complementary modalities are used. We have therefore used RGB-D data (2.5D) in particular applied to people detection. We propose a combination of independent neural network detectors specific to each modality (color & depth map) based on the same architecture (Faster RCNN). We share intermediate results of the detectors to allow them to complement and improve overall performance in difficult situations (luminosity loss or acquisition noise of the depth map). We are establishing new state of the art scores in the field and propose a more complex and richer data set to the community (ONERA.ROOM). Finally, we made use of the 3D information contained in the RGB-D images through a multi-view method. We have defined a strategy for generating 2D virtual views that are consistent with the 3D structure. For a semantic segmentation task, this approach artificially increases the training data for each RGB-D image and accumulates different predictions during the test. We obtain new reference results on the SUNRGBD and NYUDv2 datasets. All these works allowed us to handle in an original way 2D, 2.5D and 3D robotic data with neural networks. Whether for classification, detection and semantic segmentation, we not only validated our approaches on difficult data sets, but also brought the state of the art to a new level of performance
Fourure, Damien. "Réseaux de neurones convolutifs pour la segmentation sémantique et l'apprentissage d'invariants de couleur." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSES056/document.
Повний текст джерелаComputer vision is an interdisciplinary field that investigates how computers can gain a high level of understanding from digital images or videos. In artificial intelligence, and more precisely in machine learning, the field in which this thesis is positioned,computer vision involves extracting characteristics from images and then generalizing concepts related to these characteristics. This field of research has become very popular in recent years, particularly thanks to the results of the convolutional neural networks that form the basis of so-called deep learning methods. Today, neural networks make it possible, among other things, to recognize different objects present in an image, to generate very realistic images or even to beat the champions at the Go game. Their performance is not limited to the image domain, since they are also used in other fields such as natural language processing (e. g. machine translation) or sound recognition. In this thesis, we study convolutional neural networks in order to develop specialized architectures and loss functions for low-level tasks (color constancy) as well as high-level tasks (semantic segmentation). Color constancy, is the ability of the human visual system to perceive constant colours for a surface despite changes in the spectrum of illumination (lighting change). In computer vision, the main approach consists in estimating the color of the illuminant and then suppressing its impact on the perceived color of objects. We approach the task of color constancy with the use of neural networks by developing a new architecture composed of a subsampling operator inspired by traditional methods. Our experience shows that our method makes it possible to obtain competitive performances with the state of the art. Nevertheless, our architecture requires a large amount of training data. In order to partially correct this problem and improve the training of neural networks, we present several techniques for artificial data augmentation. We are also making two contributions on a high-level issue : semantic segmentation. This task, which consists of assigning a semantic class to each pixel of an image, is a challenge in computer vision because of its complexity. On the one hand, it requires many examples of training that are costly to obtain. On the other hand, it requires the adaptation of traditional convolutional neural networks in order to obtain a so-called dense prediction, i. e., a prediction for each pixel present in the input image. To solve the difficulty of acquiring training data, we propose an approach that uses several databases annotated with different labels at the same time. To do this, we define a selective loss function that has the advantage of allowing the training of a convolutional neural network from data from multiple databases. We also developed self-context approach that captures the correlations between labels in different databases. Finally, we present our third contribution : a new convolutional neural network architecture called GridNet specialized for semantic segmentation. Unlike traditional networks, implemented with a single path from the input (image) to the output (prediction), our architecture is implemented as a 2D grid allowing several interconnected streams to operate at different resolutions. In order to exploit all the paths of the grid, we propose a technique inspired by dropout. In addition, we empirically demonstrate that our architecture generalize many of well-known stateof- the-art networks. We conclude with an analysis of the empirical results obtained with our architecture which, although trained from scratch, reveals very good performances, exceeding popular approaches often pre-trained
Borne, Léonie. "Conception d’un algorithme de vision par ordinateur « top-down » dédié à la reconnaissance des sillons corticaux." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS322/document.
Повний текст джерелаWe are seven billion humans with unique cortical folding patterns. The cortical folding process occurs during the last trimester of pregnancy, during the emergence of cortical architecture. The folding patterns are impacted by architectural features specific to each individual. Hence, they could reveal signatures of abnormal developments that can lead to psychiatric syndroms. For the last 25 years, the image analysis lab of Neurospin has been designing dedicated computer vision tools to tackle the research of such signatures. The resulting tools are distributed to the community (http://brainvisa.info).This thesis has resulted in the emergence of a new generation of tools based on machine learning techniques. The first proposed tool automatically classifies local patterns of cortical folds, a problem that had never been addressed before. The second tool aims at the automatic labeling of cortical sulci by modeling the top-down recognition mechanisms necessary to overcome weaknesses of the current bottom-up systems. Thus, in addition to having higher recognition rates and shorter execution time, the proposed new model is robust to sub-segmentation errors, which is one of the greatest weaknesses of the old system. To realize these two tools, several machine learning algorithms were implemented and compared. These algorithms are inspired on the one hand by multi-atlas methods, in particular the patch approach, which are widely used for the anatomical segmentation of medical images and on the other hand by the deep learning methods that are revolutionizing the world of computer vision. The work of this thesis confirms the incredible effectiveness of deep learning techniques to adapt well to complex problems. However, the performances obtained with these techniques are generally equivalent to those of patch approaches, or even worse if the training database is limited. What makes deep learning a particularly interesting tool in practice is its fast execution, especially for the analysis of the huge databases now available
Leclerc, Sarah Marie-Solveig. "Automatisation de la segmentation sémantique de structures cardiaques en imagerie ultrasonore par apprentissage supervisé." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI121.
Повний текст джерелаThe analysis of medical images plays a critical role in cardiology. Ultrasound imaging, as a real-time, low cost and bed side applicable modality, is nowadays the most commonly used image modality to monitor patient status and perform clinical cardiac diagnosis. However, the semantic segmentation (i.e the accurate delineation and identification) of heart structures is a difficult task due to the low quality of ultrasound images, characterized in particular by the lack of clear boundaries. To compensate for missing information, the best performing methods before this thesis relied on the integration of prior information on cardiac shape or motion, which in turns reduced the adaptability of the corresponding methods. Furthermore, such approaches require man- ual identifications of key points to be adapted to a given image, which makes the full process difficult to reproduce. In this thesis, we propose several original fully-automatic algorithms for the semantic segmentation of echocardiographic images based on supervised learning ap- proaches, where the resolution of the problem is automatically set up using data previously analyzed by trained cardiologists. From the design of a dedicated dataset and evaluation platform, we prove in this project the clinical applicability of fully-automatic supervised learning methods, in particular deep learning methods, as well as the possibility to improve the robustness by incorporating in the full process the prior automatic detection of regions of interest
Частини книг з теми "Segmentation par apprentissage profond"
JACQUEMONT, Mikaël, Thomas VUILLAUME, Alexandre BENOIT, Gilles MAURIN, and Patrick LAMBERT. "Analyse d’images Cherenkov monotélescope par apprentissage profond." In Inversion et assimilation de données de télédétection, 303–35. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9142.ch9.
Повний текст джерелаТези доповідей конференцій з теми "Segmentation par apprentissage profond"
Fourcade, A. "Apprentissage profond : un troisième oeil pour les praticiens." In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206601014.
Повний текст джерела