# Towards Shape-based Knee Osteoarthritis Classification using Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1910.06119 · HTML: https://mlsanity.com/paper/1910.06119/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Geodesic B-Score for Improved Assessment of Knee Osteoarthritis](https://mlsanity.com/paper/2104.01107/) (2021)
- [Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging](https://mlsanity.com/paper/2407.21381/) (2024)

## Abstract (arXiv)

We present a transductive learning approach for morphometric osteophyte grading based on geometric deep learning. We formulate the grading task as semi-supervised node classification problem on a graph embedded in shape space. To account for the high-dimensionality and non-Euclidean structure of shape space we employ a combination of an intrinsic dimension reduction together with a graph convolutional neural network. We demonstrate the performance of our derived classifier in comparisons to an alternative extrinsic approach.