Isbn: 9783031164330 - medical image computing and computer assisted intervention – miccai 2022: 25th international conference, singapore, september 18–22, 2022, proceedings, part ii: 13432 (15 risultati)

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning - domain adaptation and generalization;Part VIII: Machine learning - weakly-supervised learning; machine learning - model interpretation; machine learning - uncertainty; machine learning theory and methodologies.…

Taschenbuch. Condizione: Neu. Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 | 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part II | Linwei Wang (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xl | Englisch | 2022 | Springer | EAN 9783031164330 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 808 | Sprache: Englisch | Produktart: Bücher | The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning ¿ domain adaptation and generalization;Part VIII: Machine learning ¿ weakly-supervised learning; machine learning ¿ model interpretation; machine learning ¿ uncertainty; machine learning theory and methodologies.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning - domain adaptation and generalization;Part VIII: Machine learning - weakly-supervised learning; machine learning - model interpretation; machine learning - uncertainty; machine learning theory and methodologies. 808 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Sing.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Computational (Integrative) Pathology.- Semi-supervised histological image segmentation via hierarchical consistency enforcement.- Federated Stain Normalization for Computational Pathology.- DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification.- ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification.- S3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification.- Distilling Knowledge from Topological Representations for Pathological Complete Response Prediction.- SETMIL: Spatial Encoding Transformer-based Multiple Instance Learning for Pathological Image Analysis.- Clinical-realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worst-case Study.- End-to-end Learning for Image-based Detection of Molecular Alterations in Digital Pathology.- S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning.- Sample hardness based gradient loss for long-tailed cervical cell detection.- Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology.- Predicting molecular traits from tissue morphology through self-interactive multi-instance learning.- InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation.- Improved Domain Generalization for Cell Detection in Histopathology Images via Test-Time Stain Augmentation.- Transformer based multiple instance learning for weakly supervised histopathology image segmentation.- GradMix for nuclei segmentation and classification in imbalanced pathology image datasets.- Spatial-hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification.- Gigapixel Whole-Slide Images Classification using Locally Supervised Learning.- Whole Slide Cervical Cancer Screening Using Graph Attention Network and Supervised Contrastive Learning.- RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization.- Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-associated Interactions between Tumor-infiltrating Lymphocytes and Tumors.- Semi-Supervised PR Virtual Staining for Breast Histopathological Images.- Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology.- Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide Images.- Test Time Transform Prediction for Open Set Histopathological Image Recognition.- Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis.- Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification.- Joint Region-Attention and Multi-Scale Transformer for Microsatellite Instability Detection from Whole Slide Images in Gastrointestinal Cancer.- Self-Supervised Pre-Training for NucleiSegmentation.- LifeLonger: A Benchmark for Continual Disease Classification.- Unsupervised Nuclei Segmentation using Spatial Organization Priors.- Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images.- MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation.- Region-guided CycleGANs for Stain Transfer in Whole Slide Images.- Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification.- Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction.- Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling.- Prostate Cancer Histology Synthesis using StyleGAN Latent Space Annotation.- Fast FF-to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning.- Feature Re-c. …