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Found 7 papers, 5 papers with code
DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction
However, current cardiac MRI-based reconstruction technology used in clinical settings is 2D with limited through-plane resolution, resulting in low-quality reconstructed cardiac volumes.
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation
The supervised learning of the proposed method extracts features from limited labeled data in each client, while the unsupervised data is used to distill both feature and response-based knowledge from a national data repository to further improve the accuracy of the collaborative model and reduce the communication cost.
Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image
For diagnosis of shoulder illness, it is essential to look at the morphology deviation of scapula and humerus from the medical images that are acquired from Magnetic Resonance (MR) imaging.
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
Data Augmentation for Object Detection via Differentiable Neural Rendering
We propose a new offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation
However, effective and efficient delineation of all the knee articular cartilages in large-sized and high-resolution 3D MR knee data is still an open challenge.
Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data
For the initial shoulder preoperative diagnosis, it is essential to obtain a three-dimensional (3D) bone mask from medical images, e. g., magnetic resonance (MR).
