Search Results for author:
Found 18 papers, 11 papers with code
VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module
This novel hybrid VMamba-CNN network is designed specifically for 3D image registration.
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
Mamba-UNet adopts a pure Visual Mamba (VMamba)-based encoder-decoder structure, infused with skip connections to preserve spatial information across different scales of the network.
MGDepth: Motion-Guided Cost Volume For Self-Supervised Monocular Depth In Dynamic Scenarios
Despite advancements in self-supervised monocular depth estimation, challenges persist in dynamic scenarios due to the dependence on assumptions about a static world.
Residual Aligner-based Network (RAN): Motion-separable structure for coarse-to-fine discontinuous deformable registration
Deformable image registration, the estimation of the spatial transformation between different images, is an important task in medical imaging.
Ranked #1 on
Image Registration
on Unpaired-lung-CT
Computed Tomography (CT)
Deformable Medical Image Registration
+2
Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant
The COVID-19 pandemic has been ongoing for nearly two and half years, and new variants of concern (VOCs) of SARS-CoV-2 continue to emerge, which urges the development of broadly neutralizing antibodies.
A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation
A dual-head mechanism is used to predict optical flow for rigid and non-rigid motion based on a divide-and-conquer manner, which significantly improves the optical flow estimation performance.
Self-Supervised Depth Estimation in Laparoscopic Image using 3D Geometric Consistency
Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system.
When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation
A topological exploration of all alternative supervision modes with CNN and ViT are detailed validated, demonstrating the most promising performance and specific setting of our method on semi-supervised medical image segmentation tasks.
Recursive Deformable Image Registration Network with Mutual Attention
Deformable image registration, estimating the spatial transformation between different images, is an important task in medical imaging.
Ranked #3 on
Image Registration
on Unpaired-lung-CT
Residual Aligner Network
Image registration is important for medical imaging, the estimation of the spatial transformation between different images.
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry
To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features which lie on the same epipolar line while learning mutual information between the input stereo pair.
Ranked #1 on
Depth Prediction
on KITTI 2015
D-Net: Siamese based Network with Mutual Attention for Volume Alignment
Alignment of contrast and non-contrast-enhanced imaging is essential for the quantification of changes in several biomedical applications.
One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud
An average point cloud-to-point cloud (PC-to-PC) error of 1. 72mm has been achieved, which is comparable to the PLSR-based (1. 42mm) and KPLSR-based (1. 31mm) two-stage shape instantiation algorithm.
Real-time 3D Shape Instantiation for Partially-deployed Stent Segment from a Single 2D Fluoroscopic Image in Robot-assisted Fenestrated Endovascular Aortic Repair
As original GCN is for classification, in this paper, the coarsening layers are removed and the softmax function at the network end is replaced with linear mapping for the regression task.
ACNN: a Full Resolution DCNN for Medical Image Segmentation
These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to image segmentation.
3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair
The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between inserted devices and aortic branches.
Estimation of Tissue Oxygen Saturation from RGB Images based on Pixel-level Image Translation
Intra-operative measurement of tissue oxygen saturation (StO2) has been widely explored by pulse oximetry or hyperspectral imaging (HSI) to assess the function and viability of tissue.
Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects
In this paper, the AAA was segmented using U-net with a small number (two) of training subjects.
