# CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography

arXiv: https://arxiv.org/abs/2402.00570 · HTML: https://mlsanity.com/paper/2402.00570/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks](https://mlsanity.com/paper/1803.04565/) (2018)
- [Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks](https://mlsanity.com/paper/1807.10597/) (2018)
- [Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease](https://mlsanity.com/paper/2101.12505/) (2021)

## Built on by (most cited first, 12 of 12)

- [DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation](https://mlsanity.com/paper/2406.01591/) (2024)
- [CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method](https://mlsanity.com/paper/2407.19894/) (2024)
- [Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection](https://mlsanity.com/paper/2508.00438/) (2025)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram](https://mlsanity.com/paper/2510.27315/) (2025)
- [Few-Shot Video Object Segmentation in X-Ray Angiography Using Local Matching and Spatio-Temporal Consistency Loss](https://mlsanity.com/paper/2601.00988/) (2026)
- [Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data](https://mlsanity.com/paper/2602.02067/) (2026)
- [CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment](https://mlsanity.com/paper/2505.17619/) (2025)
- [3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach](https://mlsanity.com/paper/2509.13358/) (2025)
- [Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis](https://mlsanity.com/paper/2602.11536/) (2026)
- [Uncertainty-Aware Concept and Motion Segmentation for Semi-Supervised Angiography Videos](https://mlsanity.com/paper/2603.00881/) (2026)
- [ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research](https://mlsanity.com/paper/2610.14523/) (2026)

## Abstract (arXiv)

Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well-developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high-quality open-access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease-related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity and by computer scientists to create computer-aided diagnostic systems to help in such assessment. In addition, baseline classification methods are proposed and analyzed, validating the functionality of CADICA and giving the scientific community a starting point to improve CAD detection.