Deep Generative Models

Name: Deep Generative Models | 4th MICCAI Workshop, DGM4MICCAI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings
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4th MICCAI Workshop, DGM4MICCAI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

Anirban Mukhopadhyay Ilkay Oksuz Sandy Engelhardt Dorit Mehrof Yixuan Yuan(Editor)

Springer (Publisher)

Published on 8. October 2024

XI, 224 pages

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978-3-031-72744-3 (ISBN)

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Content

Intro
Preface
Organization
Contents
DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels
1 Introduction
2 Methods
2.1 DeStainer
2.2 Feature Fusion Module
2.3 ReStainer
2.4 Loss Functions
3 Experiment
3.1 Dataset and Implementation Details
3.2 Results
3.3 Ablation Study
4 Conclusion
References
WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
1 Introduction
2 Background
3 Method
4 Experiments
4.1 Experimental Settings
4.2 Experimental Results
5 Conclusion
References
Energy-Based Prior Latent Space Diffusion Model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI
1 Introduction
2 Previous Work
3 Method
4 Results
4.1 Datasets and Metrics
4.2 Lumbar Vertebrae Reconstruction
4.3 Convergence in the Latent Space
5 Conclusions
References
Anatomically-Guided Inpainting for Local Synthesis of Normal Chest Radiographs
1 Introduction
2 Methods
2.1 Datasets
2.2 Chest Radiography Inpainting
2.3 Anatomically-Guided Chest Radiography Inpainting
3 Experiments
4 Results and Discussion
5 Conclusion
References
Enhancing Cross-Modal Medical Image Segmentation Through Compositionality
1 Introduction
2 Methodology
3 Experiments
4 Conclusions
References
Unpaired Modality Translation for Pseudo Labeling of Histology Images
1 Introduction
2 Methods
2.1 Data
2.2 Models
2.3 Experiments
3 Results and Discussion
4 Conclusion
References
SNAFusion: Distilling 2D Axial Plane Diffusion Priors for Sparse-View 3D Cone-Beam CT Imaging
1 Introduction
2 Background
3 Method
3.1 Main Idea
3.2 Density Initialization
3.3 Density Refinement
4 Experiments
4.1 Experimential Settings
4.2 Results and Discussion
5 Conclusion
References
SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI Data Generation in Young People
1 Introduction
2 Method
3 Experiments and Results
3.1 Experimental Setup and Dataset
3.2 Quantitative Image Quality
3.3 Uncertainty Maps as an Explainability Surrogate
3.4 Limitations and Future Directions
4 Conclusion
Appendix
References
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
1 Introduction
1.1 Related Work
1.2 Contribution
2 Methods
2.1 Denoising Diffusion Probabilistic Models
2.2 Modifying Diffusion Models for Inpainting
3 Experiments
4 Results
5 Discussion
6 Conclusion
References
Panoptic Segmentation of Mammograms with Text-to-Image Diffusion Model
1 Introduction
2 Materials and Methods
2.1 Segmentation Framework
2.2 Datasets
2.3 Implementation Details
2.4 Evaluation Metrics
3 Results
4 Discussion
References
Interactive Generation of Laparoscopic Videos with Diffusion Models
1 Introduction
2 Related Work
3 Video Generation Pipeline
3.1 StableDiffusion
3.2 ControlNet
3.3 ControlVideo
4 Experiments
4.1 Data
4.2 Training and Inference Details
4.3 Evaluation
5 Discussion and Conclusion
References
Multi-parametric MRI to FMISO PET Synthesis for Hypoxia Prediction in Brain Tumors
1 Introduction
2 Related Work
3 Method
3.1 FMISO Dataset
3.2 FDG Dataset
3.3 Data Preprocessing
3.4 Model
4 Results
5 Conclusions
References
qMRI Diffuser: Quantitative T1 Mapping of the Brain Using a Denoising Diffusion Probabilistic Model
1 Introduction
2 Method
3 Experiments
3.1 Scanning Protocol
3.2 Datasets
3.3 Network Training
3.4 Evaluation
4 Results
5 Discussion
6 Conclusion
References
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
1 Introduction
2 Methods
2.1 Latent Diffusion Model
2.2 Differential Privacy
2.3 Data and Pre-processing
2.4 Evaluation Metrics
3 Experiments and Results
3.1 Implementation Details
3.2 Results
4 Discussion and Conclusion
References
Five Pitfalls When Assessing Synthetic Medical Images with Reference Metrics
1 Introduction
2 Data and Methods
2.1 Normalization and Binning
2.2 Reference Metrics
3 Experiments and Results
3.1 Pitfall 1: Inappropriate Normalization
3.2 Pitfall 2: Similarity of Misaligned Images
3.3 Pitfall 3: Background, Foreground and Region of Interest Similarity
3.4 Pitfall 4: Error Metrics Prefer Blurred Images
3.5 Pitfall 5: Perceptual and Task-Specific Similarity
4 Discussion and Conclusion
References
Augmenting Prostate MRI Dataset with Synthetic Volumetric Images from Zone-Conditioned Diffusion Generative Model
1 Introduction
2 Methods
2.1 Generative Model
3 Experiments
3.1 Dataset
3.2 New Sample Generation
3.3 Downstream Task
4 Experiments
5 Conclusion
References
TiBiX: Leveraging Temporal Information for Bidirectional X-Ray and Report Generation
1 Introduction
1.1 Multimodal Tokenization
1.2 Multimodal Generation Framework
2 Datasets and Experiments
3 Results and Discussion
3.1 SOTA Performance Comparison
3.2 Self-comparison
3.3 Qualitative Analysis
4 Conclusion
References
Segmentation-Guided MRI Reconstruction for Meaningfully Diverse Reconstructions
1 Introduction
2 Related Work
3 Method
3.1 Background Diffusion Models and MRI-Reconstruction
3.2 Segmentation Guidance for Diverse Sampling
3.3 Data and Models
4 Experiments and Results
5 Conclusion and Discussion
References
Non-reference Quality Assessment for Medical Imaging: Application to Synthetic Brain MRIs
1 Introduction
2 Methods
2.1 Generative Network
2.2 Quality Metrics
2.3 Quality Network
2.4 Augmentation and Inference
3 Experiments and Results
3.1 Data
3.2 Setup
3.3 Results
4 Conclusion
References
LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework
1 Introduction
2 Method
2.1 Preliminary in Diffusion Models
2.2 LatentArtiFusion
3 Experiments
3.1 Implementation Details
3.2 Datasets
3.3 Comparison and Results
3.4 Downstream Classification Evaluation
4 Conclusion
References
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models
1 Introduction
2 Methods and Materials
2.1 Dataset
2.2 Data Preprocessing
2.3 Segmentation Network Architecture
2.4 3D Reconstruction of 2D Masks Using OSEM Algorithm
3 Experiment Details
4 Results and Discussion
5 Conclusion
References
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Deep Generative Models

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