Medical Applications with Disentanglements: First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

Preface
Organization
Contents
Introduction
Applying Disentanglement in the Medical Domain: An Introduction for the MAD Workshop
	1 Introduction
	2 Generative Models
	3 Disentanglement
		3.1 Definitions of Disentanglement
		3.2 The Different Types of Biases
	4 Challenges for Medical Applications
	5 Medical Applications
	6 Future Directions
		6.1 Causality and Disentanglement
		6.2 Evaluating Disentanglement
	7 Conclusion
	References
GAN-Based Approaches
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
	1 Introduction
	2 Methodology
		2.1 InfoGAN
		2.2 Hilbert-Schmidt Independence Criterion (HSIC)
		2.3 HSIC-InfoGAN
	3 Experiments
		3.1 Implementation Details
		3.2 Results
		3.3 Strategy for Hyperparameter Tuning
	4 Discussion
	References
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs
	1 Introduction
	2 Methods
	3 Experiments
		3.1 Image Compression and Quality of Reconstruction
		3.2 Disentanglement in Latent Space
		3.3 Guided Image Manipulation
		3.4 Proximity Sampling
	4 Outlook and Conclusion
	References
Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations
	1 Introduction
	2 Related Work
		2.1 Image Anonymization
		2.2 Deep Generative Models
	3 Proposed Methodology
		3.1 Generative Module
		3.2 Identity Module
		3.3 Explanatory Module
	4 Experiments and Results
		4.1 Identity Recognition and Disease Recognition
		4.2 Image Anonymization
		4.3 Generation of Counterfactual Explanations
	5 Conclusions
	References
Autoencoder-Based Approaches
Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders
	1 Introduction
	2 Methods
		2.1 Disentangling Shape from Appearance
	3 Experiments
		3.1 Data
		3.2 Training Details
		3.3 Augmentation Schemes
		3.4 Results and Discussion
	4 Conclusion
	References
Low-Rank and Sparse Metamorphic Autoencoders for Unsupervised Pathology Disentanglement
	1 Introduction
	2 Methods
		2.1 Guided Filter Regularized Metamorphic Autoencoder
		2.2 Low-Rank and Sparse Image Decomposition for Normal/Abnormal Disentanglement
	3 Experiments and Results
	4 Discussion and Conclusion
	References
Training -VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
	1 Introduction
	2 -VAE
	3 The Antagonistic Mechanism of the Reconstruction Loss and KLD Loss in -VAE
		3.1 Information Theory Perspective
		3.2 Machine Learning Perspective
	4 Aggregate a Learned Gaussian Posterior with a Decoupled Decoder
	5 Application to Skull Reconstruction and Shape Completion
		5.1 Training Curves
		5.2 Skull Reconstruction and Skull Shape Completion
	6 Discussion and Conclusion
	A  VAE Training Curve (1200 Epochs) under =100
	B  AE-Based Skull Shape Completion
	C  Matrix Notation for DKL(1)
	References
Normalizing-Flow-Based Approaches
Disentangling Factors of Morphological Variation in an Invertible Brain Aging Model
	1 Introduction
	2 Methods
		2.1 Invertible Brain Aging Model – iBAM
		2.2 Adding Sex as Another Supervised Factor
		2.3 Ordering iBAM\'s Identity Latent Space Dimensions
	3 Experiments and Results
	4 Conclusion
	References
Comparision
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI
	1 Introduction
	2 Approaches for Modeling Anomaly
		2.1 Selected Methods
		2.2 Hierarchy of Properties
	3 Experimental Setup
	4 Observations
	5 Inferences and Discussion
	6 Conclusion
	A  Appendix
		A.1  VAE
		A.2  FactorVAE
		A.3  GLOW
		A.4  SSAE
	References
Author Index