![]() ![]() Recent work has extended the functionality of such models into vision-based tasks, leading to the advent of the vision transformer 16, 19. Due to the ease with which unstructured text can be broken down into tokens, transformers have been tremendously successful at Natural Language Processing (NLP) tasks 17, 18. A significant benefit that transformers allow for is unsupervised learning from large corpora of unlabeled data to learn relationships between tokens, and then utilize this information for other downstream tasks 16. Transformer-based neural networks utilize the attention mechanism 15 to establish and define relationships between discrete units of input data known as tokens 16. Unfortunately, transfer learning with such natural images is not a universal solution, and it is known to produce suboptimal results when there exist substantial differences in the pre-training and fine-tuning datasets 14. As a result, vision models first trained in a supervised manner on natural images 13 often form the basis of models used in healthcare settings. Transfer learning is especially useful in healthcare since datasets are limited in size due to limited patient cohorts, rarity of outcomes of interest, and costs associated with generating useful labels. This technique is described as transfer learning wherein a model that is trained on a larger, possibly unrelated dataset is fine-tuned on a smaller dataset that is relevant to a problem 12. In this context, interpreting ECGs as 2D images presents an advantage due to widely available pre-trained models which often serve as starting points for modeling tasks on smaller datasets 11. CNNs must also be purpose-built to accommodate the dimensionality of incoming data, and they have been used for interpreting ECGs both as 1D waveforms and 2D images 10. Like other neural networks, CNNs are high variance constructs 8, and require large amounts of data to prevent overfitting 9. The vast majority of this work has been built upon Convolutional Neural Networks (CNNs) 7. However, the ECG is limited in scope since physicians cannot consistently identify patterns representative of disease – especially for conditions that do not have established diagnostic criteria, or in cases when such patterns may be too subtle or chaotic for human interpretation.ĭeep learning has been applied to ECG data for several diagnostic and prognostic use cases 2, 3, 4, 5, 6. Owing to its low cost, non-invasiveness, and wide applicability to cardiac disease, the ECG is a ubiquitous investigation and over 100 million ECGs are performed each year within the United States alone 1 in various healthcare settings. The electrocardiogram (ECG) is a body surface-level recording of electrical activity within the heart. The combination of the architecture and such pre-training allows for more accurate, granular explainability of model predictions. Domain specific pre-trained transformer models may exceed the classification performance of models trained on natural images especially in very low data regimes. We also find that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. We find that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We pre-trained this model on 8.5 million ECGs and then compared performance vs. We leveraged masked image modeling to create a vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. ![]() The electrocardiogram (ECG) is a ubiquitous diagnostic modality. ![]()
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