Deep Generative Models, and Data Augmentation, Labelling, and Imperfections
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Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Deep Generative Models, and Data Augmentation, Labelling, and Imperfections


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About the Book

DGM4MICCAI 2021 - Image-to-Image Translation, Synthesis.- Frequency-Supervised MRI-to-CT Image Synthesis.- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain.- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images.- Bridging the gap between paired and unpaired medical image translation.- Conditional generation of medical images via disentangled adversarial inference. -CT-SGAN: Computed Tomography Synthesis GAN.- Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference.- CaCL: class-aware codebook learning for weakly supervised segmentation on diffuse image patterns.- BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification.- Evaluating GANs in medical imaging.- DGM4MICCAI 2021 - AdaptOR challenge.- Improved Heatmap-based Landmark Detection.- Cross-domain Landmarks Detection in Mitral Regurgitation.- DALI 2021.- Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph.- Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency.- One-shot Learning for Landmarks Detection.- Compound Figure Separation of Biomedical Images with Side Loss.- Data Augmentation with Variational Autoencoders and Manifold Sampling.- Medical image segmentation with imperfect 3D bounding boxes.- Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images.- How Few Annotations are Needed for Segmentation using a Multi-planar U-Net?.- FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation.- An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning.- Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions.- Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation.- Label Noise in Segmentation Networks: Mitigation Must Deal with Bias.- DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization.- MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation.



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Product Details
  • ISBN-13: 9783030882099
  • Publisher: Springer Nature Switzerland AG
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: First Workshop, Dgm4miccai 2021, and First Workshop, Dali 2021, Held in Conjunction with Miccai 2021, Strasbourg, France, October 1, 2021, Proceedin
  • Width: 156 mm
  • ISBN-10: 3030882098
  • Publisher Date: 30 Sep 2021
  • Height: 234 mm
  • No of Pages: 296
  • Spine Width: 16 mm
  • Weight: 467 gr


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