Hamza Mekhzoum
A5011157268- Advanced Vision and Imaging
- Image Processing Techniques and Applications
- Medical Image Segmentation Techniques
- Cell Image Analysis Techniques
- Remote Sensing and LiDAR Applications
- 3D Shape Modeling and Analysis
- Advanced Neural Network Applications
- Medical Imaging and Analysis
- Advanced Image Processing Techniques
Vrije Universiteit Brussel
2022-2024
Abstract Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models used. This seen growing popularity of ShapeNet (51,300 models) Princeton ModelNet (127,915 models). However, a large collection anatomical shapes (e.g., bones, organs, vessels) 3D surgical instruments missing. Methods We present MedShapeNet translate...
Abstract We ask whether Adversariality in left-right stereo images can learn to estimate an optimal depth map through a consensus-based loss function and ego-motion. describe the workflow as merging supervised learning (AS) unsupervised (U) models. The model optimizes estimation network with prior knowledge of ground-truth maps. Inspired by recent works on adversarial neural networks, we formulate task. Thus generate two maps from images, respectively. Based their behavior–that is, function,...
Abstract We ask whether Adversariality in left-right stereo images can learn toestimate an optimal depth map through a consensus-based loss func-tion and ego-motion. describe the workow as merging supervisedlearning (AS) unsupervised learning (U). The supervised learningmodel optimizes estimation network with prior knowledge ofground-truth maps. Inspired by recent works on adversarialneural networks, we formulate model adversariallearning task. Thus generate two maps from stereoimages,...