# Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling

arXiv: https://arxiv.org/abs/2308.10320 · HTML: https://mlsanity.com/paper/2308.10320/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 20 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [PHiSeg: Capturing Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/1906.04045/) (2019)
- [Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching](https://mlsanity.com/paper/1911.11308/) (2019)
- [Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking](https://mlsanity.com/paper/2005.11081/) (2020)
- [AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms](https://mlsanity.com/paper/2301.04733/) (2023)
- [Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network](https://mlsanity.com/paper/2305.12327/) (2023)

## Abstract (arXiv)

Coronary artery disease (CAD) is one of the primary causes leading to death worldwide. Accurate extraction of individual arterial branches on invasive coronary angiograms (ICA) is important for stenosis detection and CAD diagnosis. However, deep learning-based models face challenges in generating semantic segmentation for coronary arteries due to the morphological similarity among different types of coronary arteries. To address this challenge, we propose an innovative approach using the hyper association graph-matching neural network with uncertainty quantification (HAGMN-UQ) for coronary artery semantic labeling on ICAs. The graph-matching procedure maps the arterial branches between two individual graphs, so that the unlabeled arterial segments are classified by the labeled segments, and the coronary artery semantic labeling is achieved. By incorporating the anatomical structural loss and uncertainty, our model achieved an accuracy of 0.9345 for coronary artery semantic labeling with a fast inference speed, leading to an effective and efficient prediction in real-time clinical decision-making scenarios.