Intro
Preface
Organization
Contents - Part III
Machine Learning - Explainability, Bias, and Uncertainty II
Pre-trained Diffusion Models for Plug-and-Play Medical Image Enhancement
1 Introduction
2 Method
2.1 Denoising Diffusion Probabilistic Models (DDPM) for Unconditional Image Generation
2.2 Image Enhancement with Denoising Algorithm
2.3 Pre-Trained Diffusion Models for Plug-and-play Medical Image Enhancement
3 Experiments
4 Results and Discussion
5 Conclusion
References

GRACE: A Generalized and Personalized Federated Learning Method for Medical Imaging
1 Introduction
2 Method
2.1 Overview of the GPFL Framework
2.2 Local Training Phase: Feature Alignment & Personalization
2.3 Aggregation Phase: Consistency-Enhanced Re-weighting
3 Experiments
3.1 Dataset and Experimental Setting
3.2 Comparison with SOTA Methods
3.3 Further Analysis
4 Conclusion
References
Chest X-ray Image Classification: A Causal Perspective
1 Introduction
2 Methodology
2.1 A Causal View on CXR Images
2.2 Causal Intervention via Backdoor Adjustment

2.3 Training Object
3 Experiments
3.1 Experimental Setup
3.2 Results and Analysis
4 Conclusion
References
DRMC: A Generalist Model with Dynamic Routing for Multi-center PET Image Synthesis
1 Introduction
2 Method
2.1 Center Interference Issue
2.2 Network Architecture
2.3 Dynamic Routing Strategy
2.4 Loss Function
3 Experiments and Results
3.1 Dataset and Evaluation
3.2 Implementation
3.3 Comparative Experiments
3.4 Ablation Study
4 Conclusion
References

Federated Condition Generalization on Low-dose CT Reconstruction via Cross-domain Learning
1 Introduction
2 Method
2.1 iRadonMAP
2.2 Proposed FedCG Method
3 Experiments
3.1 Dataset
3.2 Implementation Details
4 Result
4.1 Reuslt on Condition #1
4.2 Result on Condition #2
4.3 Ablation Experiments
5 Conclusion
References
Enabling Geometry Aware Learning Through Differentiable Epipolar View Translation
1 Introduction
2 Methods
3 Experiments
3.1 Model Training
4 Results
5 Discussion and Conclusion
References

Enhance Early Diagnosis Accuracy of Alzheimer's Disease by Elucidating Interactions Between Amyloid Cascade and Tau Propagation
1 Introduction
2 Method
2.1 Reaction-Diffusion Model for Neuro-Dynamics
2.2 Construction on the Interaction Between Tau and Amyloid
2.3 Neural Network Landscape of RDM-Based Dynamic Model
3 Experiments
3.1 Data Description and Experimental Setting
3.2 Ablation Study in Prediction Disease Progression
3.3 Prognosis Accuracies on Forecasting AD Risk
4 Conclusion
References