Deep Generative Models, and Data Augmentation, Labelling, and Imperfections : First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / edited by Sandy Engelhardt, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman, Yuan Xue.

Format:

Book

Contributor:

Engelhardt, Sandy, Editor.

Oksuz, Ilkay., Editor.

Zhu, Dajiang, Editor.

Yuan, Yixuan., Editor.

Mukhopadhyay, Anirban, Editor.

Heller, Nicholas, Editor.

Huang, Sharon Xiaolei., Editor.

Nguyen, Hien., Editor.

Sznitman, Raphael, Editor.

Xue, Yuan, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 13003

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 13003

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Artificial intelligence.

Bioinformatics.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Physical Description:

1 online resource (XV, 278 pages) : 104 illustrations, 82 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems. .

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-88210-5

9783030882105

Access Restriction:

Restricted for use by site license.