Expanding Domain-Specific Datasets with Stable Diffusion Generative Models for Simulating Myocardial Infarction

Areas, such as the identification of human activity, have accelerated thanks to the immense development of artificial intelligence (AI). However, the lack of data is a major obstacle to even faster progress. This is particularly true in computer vision, where training a model typically requires at least tens of thousands of images. Moreover, when the activity a researcher is interested in is far from the usual, such as falls, it is difficult to have a sufficiently large dataset. An example of this could be the identification of people suffering from a heart attack. In this sense, this work proposes a novel approach that relies on generative models to extend image datasets, adapting them to generate more domain-relevant images. To this end, a refinement to stable diffusion models was performed using low-rank adaptation. A dataset of 100 images of individuals simulating infarct situations and neutral poses was created, annotated, and used. The images generated with the adapted models were evaluated using learned perceptual image patch similarity to test their closeness to the target scenario. The results obtained demonstrate the potential of synthetic datasets, and in particular the strategy proposed here, to overcome data sparsity in AI-based applications. This approach can not only be more cost-effective than building a dataset in the traditional way, but also reduces the ethical concerns of its applicability in smart environments, health monitoring, and anomaly detection. In fact, all data are owned by the researcher and can be added and modified at any time without requiring additional permissions, streamlining their research.