Machine Learning in Medical Imaging
Springer International Publishing (Verlag)
978-3-031-21013-6 (ISBN)
The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
Function MRI Representation Learning via Self-Supervised Transformer for Automated Brain Disorder Analysis.- Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images using Deep Learning.- Region-Guided Channel-Wise Attention Network for Accelerated MRI Reconstruction.- Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation.- Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN.- 3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient.- A More Design-flexible Medical Transformer for Volumetric Image Segmentation.- Dcor-VLDet: A Vertebra Landmark Detection Network for Scoliosis Assessment with Dual Coordinate System.- Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping.- A Coarse-To-Fine Network for Craniopharyngioma Segmentation.- Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring.- AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine.- Memory transformers for full context and high-resolution 3D Medical Segmentation.- Whole Mammography Diagnosis via Multi-instance Supervised Discriminative Localization and Classification.- Cross Task Temporal Consistency for Semi Supervised Medical Image Segmentation.- U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration.- UNet-eVAE: Iterative refinement using VAE embodied learning for endoscopic image segmentation.- Dynamic Linear Transformer for 3D Biomedical Image Segmentation.- Automatic Grading of Emphysema by Combining 3D Lung Tissue Appearance and Deformation Map Using a Two-stream Fully Convolutional Neural Network.- A Novel Two-Stage Multi-View Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship between Brain Function and Structure.- Fast Image-Level MRI Harmonization via Spectrum Analysis.- CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification.- Harmonization of Multi-Site Cortical Data Across the Human Lifespan.- Head and neck vessel segmentation with connective topology using affinity graph.- Coarse Retinal Lesion Annotations Refinement via Prototypical Learning.- Nuclear Segmentation and Classification: On Color & Compression Generalization.- Understanding Clinical Progression of Late-Life Depression to Alzheimer's Disease Over 5 Years with Structural MRI.- ClinicalRadioBERT: Knowledge-Infused Few Shot Learning for Clinical Notes Named Entity Recognition.- Graph Representation Neural Architecture Search for Optimal Spatial/Temporal Functional Brain Network Decomposition.- Driving Points Prediction For Abdominal Probabilistic Registration.- CircleSnake: Instance Segmentation with Circle Representation.- Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle.- Coronary Ostia Localization Using Residual U-Net with HeatmapMatching and 3D DSNT.- AMLP-Conv, a 3D Axial Long-range Interaction Multilayer Perceptron for CNNs.- Neural State-Space Modeling with Latent Causal-Effect Disentanglement.- Adaptive Unified Contrastive Learning for Imbalanced Classification.- Prediction of HPV-Associated Genetic Diversity for Squamous Cell Carcinoma of Head and Neck Cancer based on 18F-FDG PET/CT.- TransWS: Transformer-based Weakly Supervised Histology Image Segmentation.- Contextual Attention Network: Transformer Meets U-Net.- Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis.- A New Lightweight Architecture and a Class Imbalance Aware Loss Function for Multi-label Classification of Intracranial Hemorrhages.- Spherical Transformer on Cortical Surfaces.- Accurate localization of inner ear regions of interests using deep reinforcement learning.- Shifted Windows Transformers for Medical Image Quality Assessment.- Multi-scale Multi-structure Siamese Network (MMSNet) for Primary Open-angleGlaucoma Prediction.- HealNet - Self-Supervised Acute Wound Heal-Stage Classification.- Federated Tumor Segmentation with Patch-wise Deep Learning Model.- Multi-scale and Focal Region Based Deep Learning Network for Fine Brain Parcellation.
| Erscheinungsdatum | 17.12.2022 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | XIII, 479 p. 173 illus., 163 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 743 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Artificial Intelligence • big medical imaging data analytics • Bioinformatics • cellular image analysis • Computer-aided Diagnosis • Computer Networks • Computer Science • Computer systems • computer vision • Deep learning • Digital Pathology • Education • Engineering • Image Analysis • Image Processing • Image Quality • image reconstruction • Image Retrieval • Image Segmentation • Internet • learning • machine learning • Mathematics • Medical Images • Molecular Imaging • multi-modality fusion • Neural networks • pattern recognition |
| ISBN-10 | 3-031-21013-1 / 3031210131 |
| ISBN-13 | 978-3-031-21013-6 / 9783031210136 |
| Zustand | Neuware |
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