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.).

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 tak...

Full description

Saved in:

Bibliographic Details
Corporate Authors:	DGM4MICCAI (Workshop) Online), DALI (Workshop), International Conference on Medical Image Computing and Computer-Assisted Intervention
Other Authors:	Engelhardt, Sandy, Oksuz, Ilkay, Zhu, Dajiang, Yuan, Yixuan, Mukhopadhyay, Anirban, Heller, Nicholas (Doctoral student), Huang, Sharon Xiaolei, Nguyen, Hien, Sznitman, Raphael, Xue, Yuan
Format:	eBook
Language:	English
Published:	Cham : Springer, 2021.
Series:	Lecture notes in computer science ; 13003. LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics.
Subjects:	Diagnostic imaging > Data processing > Congresses. Diagnostic imaging > Data processing proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès.
Online Access:	Click for online access

Table of 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

Similar Items