# Trusted Multi-View Deep Learning Classification of Fetal Congenital Heart Disease with Feature-level and Decision-level Fusion

arXiv: https://arxiv.org/abs/2606.15265 · HTML: https://mlsanity.com/paper/2606.15265/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 13 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [Trusted Multi-View Classification](https://mlsanity.com/paper/2102.02051/) (2021)
- [CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification](https://mlsanity.com/paper/2308.03968/) (2023)
- [MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer](https://mlsanity.com/paper/2402.16298/) (2024)
- [Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification](https://mlsanity.com/paper/2104.02265/) (2021)
- [Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning](https://mlsanity.com/paper/2306.03835/) (2023)
- [Automated interpretation of congenital heart disease from multi-view echocardiograms](https://mlsanity.com/paper/2311.18788/) (2023)

## Abstract (arXiv)

Congenital heart disease (CHD) refers to the abnormal anatomical structure caused by the abnormal development of the heart and great vessels during embryonic development. Traditional diagnostics often fail to achieve high accuracy and efficiency, especially given the complexity of cardiac anatomy. This study presents a specialized multi-view deep learning framework for CHD binary classification using echocardiographic images. A large-scale CHD dataset, including five views, was used to train the model, enabling it to integrate multi-angle image data. The framework utilizes advanced feature extraction and attention mechanisms to improve diagnostic precision and reliability. An uncertainty-based decision-making component is also integrated to handle low-quality images, enhancing diagnostic outcomes. Experimental results show that this method achieves top-tier performance on our dataset and provides a robust tool for early CHD detection, underscoring its potential for clinical use. The dataset and source code will be released upon paper acceptance.