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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 / Sandy Engelhardt, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman, Yuan Xue (eds.).

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 13003. | LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.Publication details: Cham : Springer, 2021.Description: 1 online resource (285 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030882105
  • 3030882101
Other title:
  • DGM4MICCAI 2021
  • DALI 2021
Subject(s): Genre/Form: Additional physical formats: Print version:: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections.DDC classification:
  • 616.07/54 23
LOC classification:
  • RC78.7.D53
Online resources:
Contents:
Intro -- DGM4MICCAI 2021 Preface -- DGM4MICCAI 2021 Organization -- DALI 2021 Preface -- DALI 2021 Organization -- Contents -- Image-to-Image Translation, Synthesis -- Frequency-Supervised MR-to-CT Image Synthesis -- 1 Introduction -- 2 Method -- 2.1 Frequency-Supervised Synthesis Network -- 2.2 High-Frequency Adversarial Learning -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Results -- 4 Conclusion -- References -- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain -- 1 Introduction -- 2 Methods -- 2.1 Style Encoder -- 2.2 Content Encoder
2.3 Decoder -- 2.4 Loss Functions -- 2.5 Implementation Details -- 3 Experiments -- 3.1 Qualitative Results -- 3.2 Quantitative Results -- 4 Conclusion -- References -- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images -- 1 Introduction -- 2 Methods -- 2.1 3D-StyleGAN -- 3 Results -- 4 Discussion -- References -- Bridging the Gap Between Paired and Unpaired Medical Image Translation -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Comparison with Baselines -- 3.2 Ablation Studies -- 4 Conclusion -- References
Conditional Generation of Medical Images via Disentangled Adversarial Inference -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Dual Adversarial Inference (DAI) -- 2.3 Disentanglement Constrains -- 3 Experiments -- 3.1 Generation Evaluation -- 3.2 Style-Content Disentanglement -- 3.3 Ablation Studies -- 4 Conclusion -- A Disentanglement Constrains -- A.1 Content-Style Information Minimization -- A.2 Self-supervised Regularization -- B Implementation Details -- B.1 Implementation Details -- B.2 Generating Hybrid Images -- C Datasets -- C.1 HAM10000 -- C.2 LIDC -- D Baselines
D.1 Conditional InfoGAN -- D.2 cAVAE -- D.3 Evaluation Metrics -- E Related Work -- E.1 Connection to Other Conditional GANs in Medical Imaging -- E.2 Disentangled Representation Learning -- References -- CT-SGAN: Computed Tomography Synthesis GAN -- 1 Introduction -- 2 Methods -- 3 Datasets and Experimental Design -- 3.1 Dataset Preparation -- 4 Results and Discussion -- 4.1 Qualitative Evaluation -- 4.2 Quantitative Evaluation -- 5 Conclusions -- A Sample Synthetic CT-scans from CT-SGAN -- B Nodule Injector and Eraser -- References -- Applications and Evaluation
Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference -- 1 Introduction -- 2 Methods -- 2.1 Learning -- 2.2 Inference -- 2.3 Objectives -- 2.4 Implementation -- 3 Experiments -- 3.1 Inpainting on Live-Pig Images -- 3.2 Filling in Artifact Regions After Segmentation -- 3.3 Needle Tracking -- 4 Conclusion -- References -- CaCL: Class-Aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns -- 1 Introduction -- 2 Methods -- 2.1 Class-Aware Codebook Based Feature Encoding -- 2.2 Loss Definition -- 2.3 Training Strategy
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.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library eBook LNCS Available
Total holds: 0

Intro -- DGM4MICCAI 2021 Preface -- DGM4MICCAI 2021 Organization -- DALI 2021 Preface -- DALI 2021 Organization -- Contents -- Image-to-Image Translation, Synthesis -- Frequency-Supervised MR-to-CT Image Synthesis -- 1 Introduction -- 2 Method -- 2.1 Frequency-Supervised Synthesis Network -- 2.2 High-Frequency Adversarial Learning -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Results -- 4 Conclusion -- References -- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain -- 1 Introduction -- 2 Methods -- 2.1 Style Encoder -- 2.2 Content Encoder

2.3 Decoder -- 2.4 Loss Functions -- 2.5 Implementation Details -- 3 Experiments -- 3.1 Qualitative Results -- 3.2 Quantitative Results -- 4 Conclusion -- References -- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images -- 1 Introduction -- 2 Methods -- 2.1 3D-StyleGAN -- 3 Results -- 4 Discussion -- References -- Bridging the Gap Between Paired and Unpaired Medical Image Translation -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Comparison with Baselines -- 3.2 Ablation Studies -- 4 Conclusion -- References

Conditional Generation of Medical Images via Disentangled Adversarial Inference -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Dual Adversarial Inference (DAI) -- 2.3 Disentanglement Constrains -- 3 Experiments -- 3.1 Generation Evaluation -- 3.2 Style-Content Disentanglement -- 3.3 Ablation Studies -- 4 Conclusion -- A Disentanglement Constrains -- A.1 Content-Style Information Minimization -- A.2 Self-supervised Regularization -- B Implementation Details -- B.1 Implementation Details -- B.2 Generating Hybrid Images -- C Datasets -- C.1 HAM10000 -- C.2 LIDC -- D Baselines

D.1 Conditional InfoGAN -- D.2 cAVAE -- D.3 Evaluation Metrics -- E Related Work -- E.1 Connection to Other Conditional GANs in Medical Imaging -- E.2 Disentangled Representation Learning -- References -- CT-SGAN: Computed Tomography Synthesis GAN -- 1 Introduction -- 2 Methods -- 3 Datasets and Experimental Design -- 3.1 Dataset Preparation -- 4 Results and Discussion -- 4.1 Qualitative Evaluation -- 4.2 Quantitative Evaluation -- 5 Conclusions -- A Sample Synthetic CT-scans from CT-SGAN -- B Nodule Injector and Eraser -- References -- Applications and Evaluation

Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference -- 1 Introduction -- 2 Methods -- 2.1 Learning -- 2.2 Inference -- 2.3 Objectives -- 2.4 Implementation -- 3 Experiments -- 3.1 Inpainting on Live-Pig Images -- 3.2 Filling in Artifact Regions After Segmentation -- 3.3 Needle Tracking -- 4 Conclusion -- References -- CaCL: Class-Aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns -- 1 Introduction -- 2 Methods -- 2.1 Class-Aware Codebook Based Feature Encoding -- 2.2 Loss Definition -- 2.3 Training Strategy

2.4 Weakly Supervised Learning Segmentation.

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.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed October 7, 2021).

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