# Early Detection of Myocardial Infarction in Low-Quality Echocardiography

arXiv: https://arxiv.org/abs/2010.02281 · HTML: https://mlsanity.com/paper/2010.02281/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image](https://mlsanity.com/paper/2312.07381/) (2023)
- [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024)
- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance](https://mlsanity.com/paper/2412.15058/) (2024)
- [CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation](https://mlsanity.com/paper/2206.07664/) (2022)
- [Early Myocardial Infarction Detection over Multi-view Echocardiography](https://mlsanity.com/paper/2111.05790/) (2021)
- [Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography](https://mlsanity.com/paper/2204.07253/) (2022)
- [CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis](https://mlsanity.com/paper/2403.10164/) (2024)
- [Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?](https://mlsanity.com/paper/2305.01997/) (2023)
- [DroneNet: Crowd Density Estimation using Self-ONNs for Drones](https://mlsanity.com/paper/2211.07137/) (2022)
- [Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography](https://mlsanity.com/paper/2103.14734/) (2021)
- [Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography](https://mlsanity.com/paper/2303.06744/) (2023)
- [SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography](https://mlsanity.com/paper/2309.15520/) (2023)
- [Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification](https://mlsanity.com/paper/2402.06530/) (2024)
- [Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency Relearning](https://mlsanity.com/paper/2503.14958/) (2025)
- [CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?](https://mlsanity.com/paper/2510.00520/) (2025)
- [ProPL: Universal Semi-Supervised Ultrasound Image Segmentation via Prompt-Guided Pseudo-Labeling](https://mlsanity.com/paper/2511.15057/) (2025)
- [Two-Phase Segmentation Approach for Accurate Left Ventricle Segmentation in Cardiac MRI using Machine Learning](https://mlsanity.com/paper/2407.20387/) (2024)
- [Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains](https://mlsanity.com/paper/2512.13534/) (2025)

## Abstract (arXiv)

Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further heart tissue damages or death. The earliest and most reliable sign of ischemia is regional wall motion abnormality (RWMA) of the affected part of the ventricular muscle. Echocardiography can easily, inexpensively, and non-invasively exhibit the RWMA. In this article, we introduce a three-phase approach for early MI detection in low-quality echocardiography: 1) segmentation of the entire left ventricle (LV) wall using a state-of-the-art deep learning model, 2) analysis of the segmented LV wall by feature engineering, and 3) early MI detection. The main contributions of this study are highly accurate segmentation of the LV wall from low-quality echocardiography, pseudo labeling approach for ground-truth formation of the unannotated LV wall, and the first public echocardiographic dataset (HMC-QU)* for MI detection. Furthermore, the outputs of the proposed approach can significantly help cardiologists for a better assessment of the LV wall characteristics. The proposed approach has achieved 95.72% sensitivity and 99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03% specificity, and 86.85% precision for MI detection on the HMC-QU dataset. *The benchmark HMC-QU dataset is publicly shared at the repository https://www.kaggle.com/aysendegerli/hmcqu-dataset