TetCNN: Convolutional Neural Networks on Tetrahedral Meshes.

Convolutional neural networks (CNN) have been broadly studied on images, videos, graphs, and triangular meshes. However, it has seldom been studied on tetrahedral meshes. Given the merits of using volumetric meshes in applications like brain image analysis, we introduce a novel interpretable graph CNN framework for the tetrahedral mesh structure.

Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence.

This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics. We introduce a novel hybrid geometric deep learning-based model that learns geometrically meaningful and discretization-independent features. The proposed framework has a U-Net model as the primary node feature extractor, followed by a successive spectral-based graph convolutional network.

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing.

Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts.

Deep Learning on SDF for Classifying Brain Biomarkers.

Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an important indicator for the disease. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET).