Isbn: 9783031456725 - machine learning in medical imaging: 14th international workshop, mlmi 2023, held in conjunction with miccai 2023, vancouver, bc, canada, october 8, 2023, proceedings, part i: 14348 (9 risultati)

Condizione: New. In.

Condizione: New.

Condizione: New.

Paperback. Condizione: Brand New. 502 pages. 9.26x6.10x1.01 inches. In Stock.

Taschenbuch. Condizione: Neu. Machine Learning in Medical Imaging | 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Part I | Xiaohuan Cao (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xxii | Englisch | 2023 | Springer | EAN 9783031456725 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus onmajor trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus onmajor trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. 504 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation.- Cross-Domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Cardiac SPECT.- Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion.- Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network.- Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior.- SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap.- unORANIC: Unsupervised orthogonalization of anatomy and image-characteristic features.- An Investigation of Different Deep Learning Pipelines for GABA-edited MRS Reconstruction.- Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs.- Brain MRI to PET Synthesis and Amyloid Estimation in Alzheimer's Disease via 3D Multimodal Contrastive GAN.- Accelerated MRI Reconstruction via Dynamic Deformable Alignment based Transformer.- Deformable Cross-Attention Transformer for Medical Image Registration.- Deformable Cross-Attention Transformer for Medical Image Registration.- Implicitly solved regularization for learning-based image registration.- BHSD: A 3D Brain Hemorrhage Segmentation Dataset.- Contrastive Learning-based Breast Tumor Segmentation in DCE-MRI.- FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation.- Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms.- Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance.- Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation.- Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers.- Prostate Segmentation Using Multiparametric and Multiplanar Magnetic Resonance Images.- SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation.- Automated Coarse-to-fine Segmentation of Thoracic Duct using Anatomy Priors and Topology-guided Curved Planar Reformation.- Leveraging Self-Attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter Images.- PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation.- A Super Token Vision Transformer and CNN Parallel Branch Network for mCNV Lesion Segmentation in OCT Images.- Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images.- A Domain-free Semi-supervised Method for Myocardium Segmentation in 2D Echocardiography Sequences.- Self-Training with Domain-mixed Data for Few-Shot Domain Adaptation in Medical Image Segmentation Tasks.- Bridging the Task Barriers: Online Knowledge Distillation Across Tasks for Semi-Supervised Mediastinal Segmentation in CT.- Relational UNet for Image Segmentation.- Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data.- Improving Automated Prostate Cancer Detection and Classification Accuracy with Multi-Scale Cancer Information.- Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-Scale Dependency Modeling.- MagNET: Modality-Agnostic Network for Brain Tumor Segmentation and Characterization with Missing Modalities.- Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model.- IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease Prediction.- Multi-Modal Adapter for Medical Vision-and-Language LearSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 504 pp. Englisch.…