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  • 1
    UID:
    b3kat_BV049396788
    Format: 1 Online-Ressource (xxi, 482 Seiten) , 138 Illustrationen, 131 in Farbe
    ISBN: 9783031456763
    Series Statement: Lecture notes in computer science 14349
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-45675-6
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-45677-0
    Language: English
    Keywords: Bildverarbeitung ; Maschinelles Sehen ; Maschinelles Lernen ; Bildgebendes Verfahren ; Konferenzschrift
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    b3kat_BV049396787
    Format: 1 Online-Ressource (xxii, 480 Seiten) , 174 Illustrationen, 158 in Farbe
    ISBN: 9783031456732
    Series Statement: Lecture notes in computer science 14348
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-45672-5
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-45674-9
    Language: English
    Keywords: Bildverarbeitung ; Maschinelles Sehen ; Maschinelles Lernen ; Bildgebendes Verfahren ; Konferenzschrift
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    Book
    Book
    Shanghai : Shanghai Faxue Bianyishe
    UID:
    almafu_BV026053414
    Format: 4, 216 S.
    Edition: Xiuzheng 5 ban
    Original writing edition: 修正5版
    Original writing title: 民法總則釋義
    Original writing person/organisation: 歐陽, 谿
    Original writing publisher: 上海 : 上海法學編譯社
    Series Statement: Xianxing falü shiyi congshu
    Language: Chinese
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  • 4
    UID:
    almahu_9949709232102882
    Format: XXII, 480 p. 174 illus., 158 illus. in color. , online resource.
    Edition: 1st ed. 2024.
    ISBN: 9783031456732
    Series Statement: Lecture Notes in Computer Science, 14348
    Content: 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.
    Note: 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 Learning -- Sector Quantized Multi-modal Guidance for Alzheimer's Disease Diagnosis Based on Feature Imputation -- Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting -- Dual-stream model with brain metrics and images for MRI-based fetal brain age estimation -- PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis -- Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy -- More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data -- Tailoring Large Language Models to Radiology: A preliminary approach to LLM adaptation for a highly specialized domain.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031456725
    Additional Edition: Printed edition: ISBN 9783031456749
    Language: English
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  • 5
    UID:
    almahu_9949709232002882
    Format: XXI, 482 p. 138 illus., 131 illus. in color. , online resource.
    Edition: 1st ed. 2024.
    ISBN: 9783031456763
    Series Statement: Lecture Notes in Computer Science, 14349
    Content: 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.
    Note: 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 Brain MRI Using Style GANs to Detect Localized Neurodegeneration -- Deep Bayesian Quantization for Supervised Neuroimage Search -- Triplet Learningfor Chest X-Ray Image Search in Automated COVID-19 Analysis -- Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers -- Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning -- RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classification -- Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon -- NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images -- Regionalized Infant Brain Cortical Development Based on Multi-view, High-level fMRI Fingerprint.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031456756
    Additional Edition: Printed edition: ISBN 9783031456770
    Language: English
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  • 6
    UID:
    b3kat_BV049428780
    Series Statement: Lecture notes in computer science
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Konferenzschrift
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