Search Results for author:
Found 7 papers, 3 papers with code
Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training
We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions.
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration.
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs).
CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images
We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation.
EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation
3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results.
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation.
Image reconstruction from limited range projections using orthogonal moments
A set of orthonormal polynomials is proposed for image reconstruction from projection data.
