03467nam 2200577 450 991046670530332120200520144314.02-7598-2271-010.1051/978-2-7598-2271-3(CKB)4100000007121442(MiAaPQ)EBC5567267(PPN)233362193(Au-PeEL)EBL5567267(OCoLC)1060583387(DE-B1597)574955(DE-B1597)9782759822713(MiAaPQ)EBC6810485(Au-PeEL)EBL6810485(EXLCZ)99410000000712144220181217d2018 uy 0freurcnu||||||||txtrdacontentcrdamediacrrdacarrierLe temps des neurones les horloges du cerveau /Dean Buonomano ; traduction de Michel Le BellacLes Ulis Cedex A, France :EDP Sciences,[2018]©20181 online resource (281 pages) illustrationsCollection "Une Introduction à"2-7598-2270-2 Frontmatter -- Avant-propos -- Table des matières -- 1 Introduction -- 2 De l’espace et du temps à l’espace-temps. La relativité restreinte -- 3 La géométrie riemannienne et les variétés différentielles -- 4 Espace-temps et gravitation : la relativité Générale -- 5 Les Singularités de l’espace-temps -- 6 Unification et Géométrisation -- 7 Quelques réflexions sur les mathématiques, la physique, et la vulgarisation -- Annexe : Images des mathématiques de la relativité générale -- BibliographieQu’est-ce que le temps ? L’écoulement du temps est-il une illusion ? Le temps a-t-il une flèche ? Peut-on voyager dans le temps ? Est-ce que le libre arbitre existe ou bien le futur est-il prédéterminé ? En s’appuyant sur les résultats les plus récents des neurosciences, ce livre expose comment on peut tenter de répondre aujourd’hui à ces questions. Le cerveau humain est un organe complexe qui ne se contente pas de percevoir le temps, il le construit. Il fabrique notre sens de la chronologie et c’est aussi une machine à explorer le temps qui nous permet de simuler les événements passés et futurs, une faculté qui a joué un rôle déterminant dans l’évolution de l’humanité. L’ouvrage combine neurosciences, physique et philosophie. La première partie décrit les différents types d’horloges biologiques qui permettent au cerveau de percevoir le temps. La deuxième partie est consacrée à la nature du temps et à la manière dont la physique en rend compte, en particulier dans le cadre des relativités d’Einstein. Le livre se conclut en développant les aspects psychologiques de la perception du temps et la façon dont la conscience fait le lien entre le passé et l’avenir.Collection "Une introduction à."Biological rhythmsTimePhilosophyTimePsychological aspectsElectronic books.Biological rhythms.TimePhilosophy.TimePsychological aspects.612.022Buonomano Dean1046030Le Bellac MichelMiAaPQMiAaPQMiAaPQBOOK9910466705303321Le temps des neurones2472686UNINA11976nam 22007215 450 991034927400332120251113181317.03-030-32239-410.1007/978-3-030-32239-7(CKB)4100000009522938(DE-He213)978-3-030-32239-7(MiAaPQ)EBC5967936(PPN)244831130(EXLCZ)99410000000952293820191009d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I /edited by Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XXXVII, 819 p. 345 illus., 294 illus. in color.)Image Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;11764Includes index.3-030-32238-6 Optical Imaging -- Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Trials -- A Deep Reinforcement Learning Framework for Frame-by-frame Plaque Tracking on Intravascular Optical Coherence Tomography Image -- Multi-Index Optic Disc Quantification via MultiTask Ensemble Learning -- Retinal Abnormalities Recognition Using Regional Multitask Learning -- Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening -- Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces -- 3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis -- Limited-Angle Diffuse Optical Tomography Image Reconstruction using Deep Learning -- Data-driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks -- Dual Encoding U-Net for Retinal Vessel Segmentation -- A Deep Learning Design for improving Topology Coherence in Blood Vessel Segmentation -- Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation -- Unsupervised Ensemble Strategy for Retinal Vessel Segmentation -- Fully convolutional boundary regression for retina OCT segmentation -- PM-NET: Pyramid Multi-Label Network for Optic Disc and Cup Segmentation -- Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging -- Task Adaptive Metric Space for Medium-Shot Medical Image Classification -- Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization -- Deep Multi Label Classification in Affine Subspaces -- Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss -- A Divide-and-Conquer Approach towards Understanding Deep Networks -- Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography -- Active Appearance Model Induced Generative Adversarial Networks for Controlled Data Augmentation -- Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network -- Probabilistic Atlases to Enforce Topological Constraints -- Synapse-Aware Skeleton Generation for Neural Circuits -- Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation -- EDA-Net: Dense Aggregation of Deep and Shallow Information Achieves Quantitative Photoacoustic Blood Oxygenation Imaging Deep in Human Breast -- Fused Detection of Retinal Biomarkers in OCT Volumes -- Vessel-Net: Retinal Vessel Segmentation under Multi-path Supervision -- Ki-GAN: Knowledge Infusion Generative Adversarial Network for Photoacoustic Image Reconstruction in vivo -- Uncertainty guided semisupervised segmentation of retinal layers in OCT images -- Endoscopy -- Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classification -- Selective Feature Aggregation Network with Area-boundary Constraints for Polyp Segmentation -- Deep Sequential Mosaicking of Fetoscopic Videos -- Landmark-guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection -- Multi-View Learning with Feature Level Fusion for Cervical Dysplasia Diagnosis -- Real-time Surface Deformation Recovery from Stereo Videos -- Microscopy -- Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier -- From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification -- Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes -- Improving Nuclei/Gland Instance Segmentation in Histopathology Images by Full Resolution Neural Network and Spatial Constrained Loss -- Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification -- Cell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-Rate -- Accelerated ML-assisted Tumor Detection in High-Resolution Histopathology Images -- Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype -- Pathology-aware deep network visualization and its application in glaucoma image synthesis -- CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels -- ET-Net: A Generic Edge-Attention Guidance Network for Medical Image Segmentation -- Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints -- Diverse Multiple Prediction on Neural Image Reconstruction -- Deep Segmentation-Emendation Model for Gland Instance Segmentation -- Fast and Accurate Electron Microscopy Image Registration with 3D Convolution -- PlacentaNet: Automatic Morphological Characterization of Placenta Photos with Deep Learning -- Deep Multi-Instance Learning for survival prediction from Whole Slide Images -- High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and Lesions -- Deep Instance-Level Hard Negative Mining Model for Histopathology Images -- Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells -- Patch Transformer for Multi-tagging Whole Slide Histopathology Images -- Pancreatic Cancer Detection in Whole Slide Images Using Noisy Label Annotations -- Encoding histopathological WSIs using GNN for scalable diagnostically relevant regions retrieval -- Local and Global Consistency Regularized Mean Teacher for Semi-supervised Nuclei Classification -- Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer -- Precise Separation of Adjacent Nuclei using a Siamese Neural Network -- PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis -- DeepACE: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks -- Unsupervised Subtyping of Cholangiocarcinoma Using A Deep Clustering Convolutional Autoencoder -- Evidence Localization for Pathology Images using Weakly Supervised Learning -- Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework -- GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy -- IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation -- Weakly Supervised Cell Segmentation in Dense by Propagating from Detection Map -- Understanding Fixation in Fluorescence Microscopy via Robust Non-negative Tensor Factorization, Atlas-based Motion Correction and Functional Statistics -- ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection, and Cell Classification in Multiplex Immunohistochemistry Images -- Multi-task learning of a deep K-nearest neighbour network for histopathological image classification and retrieval -- Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy images -- Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&E Stained Images -- Nuclei Segmentation in Histopathological Images using Two-Stage Learning -- ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths -- CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation -- PseudoEdgeNet: Nuclei Segmentation only with Point Annotations -- Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification -- Progressive Learning for Neuronal Population Reconstruction from Optical Microscopy Images -- Whole-Sample Mapping of Cancerous and Benign Tissue Properties -- Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification -- Fine-Scale Vessel Extraction in Fundus Images by Registration with Fluorescein Angiography -- DME-Net: Diabetic Macular Edema Grading by Auxiliary Task Learning -- Attention Guided Network for Retinal Image Segmentation -- An unsupervised domain adaptation approach to classification of stem cell-derived cardiomyocytes.The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.Image Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;11764Computer visionPattern recognition systemsArtificial intelligenceMedical informaticsComputer VisionAutomated Pattern RecognitionArtificial IntelligenceHealth InformaticsComputer vision.Pattern recognition systems.Artificial intelligence.Medical informatics.Computer Vision.Automated Pattern Recognition.Artificial Intelligence.Health Informatics.616.07540285616.0757Shen Dinggangedthttp://id.loc.gov/vocabulary/relators/edtLiu Tianmingedthttp://id.loc.gov/vocabulary/relators/edtPeters Terry Medthttp://id.loc.gov/vocabulary/relators/edtStaib Lawrence Hedthttp://id.loc.gov/vocabulary/relators/edtEssert Carolineedthttp://id.loc.gov/vocabulary/relators/edtZhou Seanedthttp://id.loc.gov/vocabulary/relators/edtYap Pew-Thianedthttp://id.loc.gov/vocabulary/relators/edtKhan Aliedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910349274003321Medical Image Computing and Computer Assisted Intervention – MICCAI 20192513530UNINA