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Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health

Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health


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About the Book

Domain Adaptation and Representation Transfer.- A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis.- Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning.- FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation.- Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation.- Self-Supervised Multimodal Generalized Zero Shot Learning For Gleason Grading.- Self-Supervised Learning of Inter-Label Geometric Relationships For Gleason Grade Segmentation.- Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training.- Transductive image segmentation: Self-training and effect of uncertainty estimation.- Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation.- Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation.- Exploring Deep Registration Latent Spaces.- Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift.- Unsupervised Domain Adaption via Similarity-based Prototypes for Cross-Modality Segmentation.- A ordable AI and Healthcare.- Classification and Generation of Microscopy Images with Plasmodium Falciparum via Arti cial Neural Networks using Low Cost Settings.- Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN.- Low-Dose Dynamic CT Perfusion Denoising without Training Data.- Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory.- COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19Patient Cases from Point-of-care Ultrasound Imaging.- Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning.- Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks.- Continual Domain Incremental Learning for Chest X-ray Classification in Low-Resource Clinical Settings.- Deep learning based Automatic detection of adequately positioned mammograms.- Can non-specialists provide high quality Gold standard labels in challenging modalities.


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Product Details
  • ISBN-13: 9783030877217
  • Publisher: Springer Nature Switzerland AG
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Third Miccai Workshop, Dart 2021, and First Miccai Workshop, Fair 2021, Held in Conjunction with Miccai 2021, Strasbourg, France, September 27 and O
  • Width: 156 mm
  • ISBN-10: 3030877213
  • Publisher Date: 24 Sep 2021
  • Height: 234 mm
  • No of Pages: 282
  • Spine Width: 15 mm
  • Weight: 449 gr


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Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health
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