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Found 9 papers, 0 papers with code
Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation
The components of translator, style encoder, and style generator constitute a diversified image generator.
Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation
Extensive experiments in simulated and real data show that our model significantly outperforms existing state-of-the-art models without learning from paired images or prior individual knowledge.
MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan
Furthermore, we take the transfer results as additional training data for fluid segmentation to prove the advantage of our model indirectly, i. e., in the task of data adaptation and augmentation.
X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph
Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions.
Atlas-aware ConvNetfor Accurate yet Robust Anatomical Segmentation
Second, we can largely boost the robustness of existing ConvNets, proved by: (i) testing on scans with synthetic pathologies, and (ii) training and evaluation on scans of different scanning setups across datasets.
Exploring Instance-Level Uncertainty for Medical Detection
The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines.
Oral-3D: Reconstructing the 3D Bone Structure of Oral Cavity from 2D Panoramic X-ray
In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch.
T-Net: Learning Feature Representation with Task-specific Supervision for Biomedical Image Analysis
The encoder-decoder network is widely used to learn deep feature representations from pixel-wise annotations in biomedical image analysis.
CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion
Label propagation is a popular technique for anatomical segmentation.
