Search Results for author:
Found 7 papers, 1 papers with code
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images.
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction
Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation.
Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation
Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS).
Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle
3D shape instantiation which reconstructs the 3D shape of a target from limited 2D images or projections is an emerging technique for surgical intervention.
U-Net Training with Instance-Layer Normalization
However, two potential issues exist in BIN: first, the Clip function is not differentiable at input values of 0 and 1; second, the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN.
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.
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.
