Machine Learning in Medical Imaging

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.…