6 papers
Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels
Tongxu Zhang
Knee osteoarthritis (OA) assessment contains a natural label hierarchy between binary disease status and Kellgren--Lawrence (KL) severity. We study whether supervision at these two…
LM-CartSeg: Automated Segmentation of Lateral and Medial Cartilage and Subchondral Bone for Radiomics Analysis
Tongxu Zhang, Zongpan Li, Aaron Kam Lun Leung +1
Background and Objective: Radiomics of knee MRI requires robust, anatomically meaningful regions of interest (ROIs) that jointly capture cartilage and subchondral bone. Most existi…
Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstruction
Tongxu Zhang, Bei Wang
High-fidelity 3D anatomical reconstruction is a prerequisite for downstream clinical tasks such as preoperative planning, radiotherapy target delineation, and orthopedic implant de…
A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variation
Tongxu Zhang, Zhiming Liang, Bei Wang
Point clouds have become an increasingly important representation for 3D medical imaging, offering a compact, surface-preserving alternative to traditional voxel or mesh-based appr…
Rethinking Data Input for Point Cloud Upsampling
Tongxu Zhang
Point cloud upsampling is crucial for tasks like 3D reconstruction. While existing methods rely on patch-based inputs, and there is no research discussing the differences and princ…
Representation Learning of Point Cloud Upsampling in Global and Local Inputs
Tongxu Zhang, Bei Wang
In recent years, point cloud upsampling has been widely applied in tasks such as 3D reconstruction and object recognition. This study proposed a novel framework, ReLPU, which enhan…