Machine Learning in Medical Imaging: 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, ... II: 14349 (Lecture Notes in Computer Science) - Tapa blanda
Sinopsis
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 on major 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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Machine Learning in Medical Imaging (eng)
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Machine Learning in Medical Imaging: 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, ... Part II (Lecture Notes in Computer Science)
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Machine Learning in Medical Imaging
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Springer Nature Switzerland, Springer Nature Switzerland Okt 2023, 2023
ISBN 10: 3031456750
ISBN 13: 9783031456756
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783031456756
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Machine Learning in Medical Imaging: 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Part II
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Machine Learning in Medical Imaging
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Springer Nature Switzerland, 2023
ISBN 10: 3031456750
ISBN 13: 9783031456756
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. Nº de ref. del artículo: 1094388969
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Machine Learning in Medical Imaging: 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, . II: 14349 (Lecture Notes in Computer Science)
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Condición: New. pp. 504 1st ed. 2024 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26398553687
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Machine Learning in Medical Imaging: 14th International Workshop, Mlmi 2023, Held in Conjunction With Miccai 2023, Vancouver, Bc, Canada, October 8, 2023, Proceedings
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Machine Learning in Medical Imaging
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ISBN 10: 3031456750
ISBN 13: 9783031456756
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis.- Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder.- LMT: Longitudinal Mixing Training a Framework for the Prediction of Disease Progression Using a Single Image.- Identifying Alzheimer's Disease-induced Topology Alterations in Structural Networks using Convolutional Neural Networks.- Specificity-Aware Federated Graph Learning for Brain Disorder Analysis with Functional MRI.- 3D Transformer Based on Deformable Patch Location for Differential Diagnosis Between Alzheimer's Disease and Frontotemporal Dementia.- Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image Analysis.- Cross-view Contrastive Mutual Learning across Masked Autoencoders for Mammography Diagnosis.- Modeling Life-span Brain Age from Large-scale Dataset based on Multi-levelInformation Fusion.- Boundary-Constrained Graph Network for Tooth Segmentation on 3D Dental Surfaces.- FAST-Net: A Coarse-to-fine Pyramid Network for Face-Skull Transformation.- Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping.- Consistency Loss for Improved Colonoscopy Landmark Detection with Vision Transformers.- Radiomics Boosts Deep Learning Model for IPMN Classification.- Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection.- Enhancing Anomaly Detection in Melanoma Diagnosis through Self-Supervised Training and Lesion Comparison.- DynBrainGNN: Towards Spatio-Temporal Interpretable Graph Neural Network based on Dynamic Brain Connectome for Psychiatric Diagnosis.- Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs.- Towards Unified Modality Understanding for Alzheimer's Disease Diagnosis using Incomplete Multi-Modality Data.- COVID-19 Diagnosis Based on Swin Transformer Model with Demographic Information Fusion and Enhanced Multi-head Attention Mechanism.- MoViT: Memorizing Vision Transformers for Medical Image Analysis.- Fact-Checking of AI-Generated Reports.- Is Visual Explanation with Grad-CAM More Reliability for Deeper Neural Networks a Case Study with Automatic Pneumothorax Diagnosis.- Group Distributionally Robust Knowledge Distillation.- A Bone Lesion Identification Network (BLIN) in Whole Body CT Images.- Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment.- Data-driven Classification of Fatty Liver From 3D Unenhanced Abdominal CT Scans.- Replica-based Federated Learning with Heterogeneous Architectures for Graph Super-Resolution.- A Multitask Deep Learning Model for Voxel-level Brain Age Estimation.- Deep Nearest Neighbors for Anomaly Detectionin Chest X-Rays.- CCMix: Curriculum of Class-wise Mixup for Long-tailed Medical Image Classification.- MEDKD: Enhancing Medical Image Classification with Multiple Expert Decoupled Knowledge Distillation for Long-Tail Data.- Leveraging Ellipsoid Bounding Shapes and Fast R-CNN for Enlarged Perivascular Spaces Detection and Segmentation.- Non-Uniform Sampling-Based Breast Cancer Classification.- A Scaled Denoising Attention-based Transformer for Breast Cancer Detection and Classification.- Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes.- Delving into Ipsilateral Mammogram Assessment under Multi-View Network.- ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance.- Normative Aging for an Individual's Full BrSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 504 pp. Englisch. Nº de ref. del artículo: 9783031456756
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Machine Learning in Medical Imaging | 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Part II
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Taschenbuch. Condición: 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 II | Xiaohuan Cao (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xxi | Englisch | 2023 | Springer | EAN 9783031456756 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 127633308
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Machine Learning in Medical Imaging : 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Part II
Publicado por
Springer Nature Switzerland, Springer Nature Switzerland, 2023
ISBN 10: 3031456750
ISBN 13: 9783031456756
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Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783031456756
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