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