# Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography Databases

arXiv: https://arxiv.org/abs/2504.07606 · HTML: https://mlsanity.com/paper/2504.07606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Vision Transformer for Small-Size Datasets](https://mlsanity.com/paper/2112.13492/) (2021)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)
- [Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders](https://mlsanity.com/paper/2310.20704/) (2023)
- [EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography](https://mlsanity.com/paper/2209.04242/) (2022)
- [Enhancing Performance of Vision Transformers on Small Datasets through Local Inductive Bias Incorporation](https://mlsanity.com/paper/2305.08551/) (2023)
- [Higher Order Dynamic Mode Decomposition: from Fluid Dynamics to Heart Disease Analysis](https://mlsanity.com/paper/2201.03030/) (2022)
- [ModelFLOWs-app: data-driven post-processing and reduced order modelling tools](https://mlsanity.com/paper/2305.17150/) (2023)
- [Hierarchical Vision Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2304.00177/) (2023)
- [Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets](https://mlsanity.com/paper/2404.19579/) (2024)
- [EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach](https://mlsanity.com/paper/2411.16227/) (2024)

## Abstract (arXiv)

Heart diseases remain the leading cause of mortality worldwide, implying approximately 18 million deaths according to the WHO. In particular, heart failures (HF) press the healthcare industry to develop systems for their early, rapid, and effective prediction. This work presents an automatic system based on a novel framework which combines Modal Decomposition and Masked Autoencoders (MAE) to extend the application from heart disease classification to the more challenging and specific task of heart failure time prediction, not previously addressed to the best of authors' knowledge. This system comprises two stages. The first one transforms the data from a database of echocardiography video sequences into a large collection of annotated images compatible with the training phase of machine learning-based frameworks and deep learning-based ones. This stage includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm for both data augmentation and feature extraction. The second stage builds and trains a Vision Transformer (ViT). MAEs based on a combined scheme of self-supervised (SSL) and supervised learning, so far barely explored in the literature about heart failure prediction, are adopted to effectively train the ViT from scratch, even with scarce databases. The designed neural network analyses in real-time images from echocardiography sequences to estimate the time of happening a heart failure. This approach demonstrates to improve prediction accuracy from scarce databases and to be superior to several established ViT and Convolutional Neural Network (CNN) architectures. The source code will be incorporated into the next version release of the ModelFLOWs-app software (https://github.com/modelflows/ModelFLOWs-app).