Machine Learning in Medical Imaging
Springer International Publishing (Verlag)
978-3-031-45675-6 (ISBN)
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.
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.
| Erscheinungsdatum | 17.10.2023 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | XXI, 482 p. 138 illus., 131 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 755 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Schlagworte | Artificial Intelligence • Bioinformatics • Computer Networks • Computer Science • Computer systems • computer vision • Deep learning • Education • Engineering • Image Analysis • Image Processing • Image Quality • Image Segmentation • Internet • learning • machine learning • Mathematics • Medical Images • Neural networks • pattern recognition |
| ISBN-10 | 3-031-45675-0 / 3031456750 |
| ISBN-13 | 978-3-031-45675-6 / 9783031456756 |
| Zustand | Neuware |
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