Description
Book Synopsis.- MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis.
.- GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-Enhanced
UNet for Synthetic CT Generation from CBCT.
.- 2D to 3D MR Image Super-Resolution using Cross-Contrast Guidance.
.- 3D Super-Resolution for Enhancing Compression Fracture Detection in Thick Slice CT: Diffusion Models vs GANs.
.- From Tissue-Mimicking Phantoms to Physics-Based Scans: Synthetic OCT for
Few-Shot Foundation Model Training.
.- Multi-modal Brain MRI Synthesis with nnU-Net: Exploring Segmentation Per formance and Cross-Modality Relationships.
.- Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning.
.- From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collima tors via Hough Transform.
.- VIOLET: A framework for combined Volumetric Image registration via Opti mization and Learning for Efficient image Translation.
.- Generation of Controllable and Photorealistic Synthetic Cataract Surgery Im ages: Blending 3D Models and Real-World Data.
.- Unsupervised MRI Harmonization via Parameter Prediction and Super-Resolved
MPMs.
.- Learning Mechanistic Subtypes of Neurodegeneration with a Physics-Informed
Variational Autoencoder Mixture Model.
.- FastDTI: A 3D Scale-arbitrary Super-resolution Autoencoder Residual Dense
Network for DTI.
.- Lesion-Aware CT-to-MRI Synthesis using a Mask-Informed Diffusion with Adaptive Weighted Loss (MIDAS).
.-Conditional Iterative α-(de)Blending Model for CBCT-tosCT Synthesis: Towards a Deterministic and Simple Process.
.- Synthesizing Accurate and Realistic T1-weighted Contrast-Enhanced MR Images using Posterior-Mean Rectified Flow.
.- Clustering-based Stain Augmentation: Templates for Periodic Acid-Schiff Biopsy
Images.
