# VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation

arXiv: https://arxiv.org/abs/2408.02888 · HTML: https://mlsanity.com/paper/2408.02888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 6 Aug 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://mlsanity.com/paper/1908.03930/) (2019)

## Built on by (most cited first, 1 of 1)

- [EcoScaleNet: A Lightweight Multi Kernel Network for Long Sequence 12 lead ECG Classification](https://mlsanity.com/paper/2510.24748/) (2025)

## Abstract (arXiv)

An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditions. In practice, ECG data is stored as either digitized signals or printed images. Despite the emergence of numerous deep learning models for digitized signals, many hospitals prefer image storage due to cost considerations. Recognizing the unavailability of raw ECG signals in many clinical settings, we propose VizECGNet, which uses only printed ECG graphics to determine the prognosis of multiple cardiovascular diseases. During training, cross-modal attention modules (CMAM) are used to integrate information from two modalities - image and signal, while self-modality attention modules (SMAM) capture inherent long-range dependencies in ECG data of each modality. Additionally, we utilize knowledge distillation to improve the similarity between two distinct predictions from each modality stream. This innovative multi-modal deep learning architecture enables the utilization of only ECG images during inference. VizECGNet with image input achieves higher performance in precision, recall, and F1-Score compared to signal-based ECG classification models, with improvements of 3.50%, 8.21%, and 7.38%, respectively.