# Direct Quantification for Coronary Artery Stenosis Using Multiview Learning

arXiv: https://arxiv.org/abs/1907.10032 · HTML: https://mlsanity.com/paper/1907.10032/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [FeDETR: a Federated Approach for Stenosis Detection in Coronary Angiography](https://mlsanity.com/paper/2409.14268/) (2024)
- [Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data](https://mlsanity.com/paper/2602.02067/) (2026)
- [A Federated Learning Framework for Stenosis Detection](https://mlsanity.com/paper/2310.19445/) (2023)

## Abstract (arXiv)

The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery disease diagnosis and intervention treatment. It aims to quantify the morphological indices of the coronary artery lesions such as minimum lumen diameter, reference vessel diameter, lesion length, and these indices are the reference of the interventional stent placement. In this study, we propose a direct multiview quantitative coronary angiography (DMQCA) model as an automatic clinical tool to quantify the coronary artery stenosis from X-ray coronary angiography images. The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation. The multi-view module comprehensively learns the Spatio-temporal features of coronary arteries through a three-dimensional convolution. The attention mechanisms of each view focus on the subtle feature of the lesion region and capture the important context information. The key-frame module learns the subtle features of the stenosis through successive dilated residual blocks. The regression module finally generates the indices estimation from multiple features.