Intro
Additional Editors
CLIP Preface
CLIP Organization
FAIMI Preface
FAIMI Organization
EPIMI Preface
EPIMI Organization
Contents
CLIP
Automated Hand Joint Classification of Psoriatic Arthritis Patients Using Routinely Acquired Near Infrared Fluorescence Optical Imaging
1 Introduction
2 Background
3 Method
4 Results
5 Discussion and Future Work
References
Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors
1 Introduction
2 Methods
2.1 Datasets and Preprocessing

2.2 Models Training and Evaluation
3 Experimental Results
3.1 Neurocranial Landmark Coordinates Prediction
3.2 3DMM Validation
3.3 Ablation Study
4 Discussion and Conclusions
References
Subject-Specific Modelling of Knee Joint Motion for Routine Pre-operative Planning
1 Introduction
2 Method
2.1 Contact Surface Model of PF and TF Joint
2.2 Computation of Knee Flexion Angle
2.3 Matching Tibia and Patella Poses
3 Experiments and Discussions
3.1 Evaluation of Generated Patella and Tibia Poses
3.2 Evaluation of Tibia and Patella Pose Matching

4 Conclusion
References
Towards Fine-Grained Polyp Segmentation and Classification
1 Introduction
2 Method
2.1 Swin Transformer Encoder
2.2 Multi-Scale Feature Enhancement
2.3 Patch-Expanding Decoder
2.4 Upsample Head
2.5 Loss Function
3 PolypSegm-ASH Dataset
4 Results
4.1 Experiments on PolypSegm-ASH
4.2 Experiments on Binary Polyp Segmentation
4.3 Ablation Study. Effect of Up-Samples Before Predictions
5 Conclusion
References
Automated Orientation and Registration of Cone-Beam Computed Tomography Scans
1 Introduction
2 Materials

3 Proposed Method
3.1 Automated Standardized Orientation (ASO)
3.2 Automated Registration (AReg)
3.3 Evaluation Metrics
3.4 Implementation
4 Results
4.1 Orientation
4.2 Registration
5 Discussion
6 Conclusion
A Appendix
References
Deep Learning-Based Fast MRI Reconstruction: Improving Generalization for Clinical Translation
1 Introduction
2 Methods
2.1 Background
2.2 Physically-Primed DNN for MRI Reconstruction
3 Experiments
3.1 Dataset
3.2 Experimental Methodology
3.3 Results
4 Conclusions
References

Uncertainty Based Border-Aware Segmentation Network for Deep Caries
1 Introduction
2 Related Work
2.1 Dental Caries Image Segmentation
2.2 Uncertainty Quantification
3 Method
3.1 Border-Aware Network Using SDF
3.2 Uncertainty Based Caries Segmentation
4 Experiments and Discussion
4.1 Dataset and Settings
4.2 Verification of SDF Effectiveness
4.3 Verification of Model Robustness
5 Conclusion
References
An Efficient and Accurate Neural Network Tool for Finding Correlation Between Gene Expression and Histological Images
1 Introduction
2 Methodology