Intro
Preface
Organization
Contents - Part I
Machine Learning with Limited Supervision
PET-Diffusion: Unsupervised PET Enhancement Based on the Latent Diffusion Model
1 Introduction
2 Method
2.1 Image Compression
2.2 Latent Diffusion Model
2.3 Implementation Details
3 Experiments
3.1 Dataset
3.2 Ablation Analysis
3.3 Comparison with State-of-the-Art Methods
3.4 Generalization Evaluation
4 Conclusion and Limitations
References
MedIM: Boost Medical Image Representation via Radiology Report-Guided Masking
1 Introduction
2 Approach

2.1 Image and Text Encoders
2.2 Report-Guided Mask Generation
2.3 Decoder for Reconstruction
2.4 Objective Function
2.5 Downstream Transfer Learning
3 Experiments and Results
3.1 Experimental Details
3.2 Comparisons with Different Pre-training Methods
3.3 Discussions
4 Conclusion
References
UOD: Universal One-Shot Detection of Anatomical Landmarks
1 Introduction
2 Method
2.1 Stage I: Contrastive Learning
2.2 Stage II: Supervised Learning
3 Experiment
3.1 Experimental Results
4 Conclusion
References

S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-Supervised Polyp Segmentation
1 Introduction
2 Methodology
2.1 Preliminaries
2.2 S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning
3 Experiments
3.1 Experimental Setup
3.2 Results and Analysis
3.3 Ablation Studies
4 Conclusion
References
Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI
1 Introduction
2 Materials and Methodology
2.1 Subjects and Image Preprocessing
2.2 Proposed Method
3 Experiment

4 Discussion
5 Conclusion and Future Work
References
Anatomy-Driven Pathology Detection on Chest X-rays
1 Introduction
2 Related Work
3 Method
3.1 Model
3.2 Inference
3.3 Training
3.4 Dataset
4 Experiments and Results
4.1 Experimental Setup and Baselines
4.2 Pathology Detection Results
5 Discussion and Conclusion
References
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
1 Introduction
2 Methods
3 Experimental Setup
4 Results
5 Conclusions
References

Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction
1 Introduction
2 Methodology
2.1 Hierarchical Disentangling Encoder (HDE)
2.2 Dense Transformer for Disentanglement (DTD)
2.3 Second-Order Disentanglement for MA Reduction (SOD-MAR)
2.4 Loss Function
3 Empirical Results
3.1 Ablation Study
3.2 Comparison to State-of-the-Art (SOTA)
4 Conclusion
References
Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection
1 Introduction
2 Method
2.1 Multi-scale Cross-restoration