Medical 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 Khan

• Record Nr.: UNINA9910349274003321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• ISBN: 3-030-32239-4
• Edizione: [1st ed. 2019.]
• Descrizione fisica: 1 online resource (XXXVII, 819 p. 345 illus., 294 illus. in color.)
• Collana: Image Processing, Computer Vision, Pattern Recognition, and Graphics, , 3004-9954 ; ; 11764
• Disciplina: 616.07540285; 616.0757
• Soggetti: Computer vision; Pattern recognition systems; Artificial intelligence; Medical informatics; Computer Vision; Automated Pattern Recognition; Artificial Intelligence; Health Informatics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.

Evolutionary Thinking Across Disciplines : Problems and Perspectives in Generalized Darwinism / / edited by Agathe du Crest, Martina Valković, André Ariew, Hugh Desmond, Philippe Huneman, Thomas A. C. Reydon

• Record Nr.: UNINA9910734866403321
• Autore: Du Crest Agathe
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
• ISBN: 9783031333583; 3031333586
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (524 pages)
• Collana: Synthese Library, Studies in Epistemology, Logic, Methodology, and Philosophy of Science, , 2542-8292 ; ; 478
• Altri autori (Persone): ValkovićMartina; AriewAndré; DesmondHugh; HunemanPhilippe; ReydonThomas A. C
• Disciplina: 501
• Soggetti: Science - Philosophy; Evolution (Biology); Biology - Philosophy; Philosophy and social sciences; Philosophy of Science; Evolutionary Biology; Philosophy of Biology; Philosophy of the Social Sciences
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 1. Generalizing Darwinism as a Topic for Multidisciplinary Debate -- Part I: How Can Disciplines Benefit from, or Contribute to, Evolutionary Frameworks?. 2. Is a Non-Evolutionary Psychology Possible? -- 3. Evolutionary Economics and the Theory of Cultural Evolution -- 4. Repetition Without Replication: Notes Towards a Theory of Cultural Adaptation -- 5. The Epistemological and Ideological Stakes of Literary Darwinism -- 6. Evolutionary Aspects of Language Change -- 7. A Community Science Model for Inter-Disciplinary Evolution Education and School Improvement -- 8. Teaching for the Interdisciplinary Understanding of Evolutionary Concepts -- Part II: Generalizations of Evolutionary Theory: Common Principles or Explanatory Structures?. 9. From Games to Graphs. Evolving Networks in Cultural Evolution -- 10. Metaphysics of Evolution: Ontology and Justification of Generalized Evolution Theory -- 11. Human Social Evolution via Four Coevolutionary Levels -- Part III: Why Should We Be Skeptical of Generalizations of Darwinism?. 12. Is Natural Selection Physical? -- 13. The Risks of Evolutionary Explanation -- 14. Evolution and Ecology of Organizations and Markets -- 15. Pluralism and Epistemic Goals: Why the Social Sciences Will (Probably) not be Synthesised by Evolutionary Theory -- 16. Equations at an Exhibition: on the Cultural Price Equation -- 17. Unlike Agents: The Role of Correlation in Economics and Biology -- Part IV: How Can Evolutionary Approaches or the Target Field be Amended?. 18. From the Modern Synthesis to the Inclusive Evolutionary Synthesis: An Einsteinian Revolution in Evolution -- 19. Darwinian/Hennigian Systematics and Evo-Devo: the Missed Rendez-vous -- 20. The Generalized Selective Environment -- 21. Adding Agency to Tinbergen’s Four Questions -- 22. Cultural Evolution Research Needs to Include Human Behavioural Ecology.
• Sommario/riassunto: This volume aims to clarify the epistemic potential of applying evolutionary thinking outside biology, and provides a survey of the current state of the art in research on relevant topics in the life sciences, the philosophy of science, and the various areas of evolutionary research outside the life sciences. By bringing together chapters by evolutionary biologists, systematic biologists, philosophers of biology, philosophers of social science, complex systems modelers, psychologists, anthropologists, economists, linguists, historians, and educators, the volume examines evolutionary thinking within and outside the life sciences from a multidisciplinary perspective. While the chapters written by biologists and philosophers of science address theoretical aspects of the guiding questions and aims of the volume, the chapters written by researchers from the other areas approach them from the perspective of applying evolutionary thinking to non-biological phenomena. Taken together, the chapters in this volume do not only show how evolutionary thinking can be fruitfully applied in various areas of investigation, but also highlight numerous open problems, unanswered questions, and issues on which more clarity is needed. As such, the volume can serve as a starting point for future research on the application of evolutionary thinking across disciplines.