Deep learning models (DLM) encounter challenges in medical image segmentation and classification tasks, primarily due to the requirement for a substantial volume of annotated images, which are both time-consuming and expensive to acquire. In our work, we utilize neural style transfer (NST) to enhance a tiny dataset of ultrasound images, significantly improving the performance of deep learning models (DLM). Additionally, we explore style interpolation to generate new target styles, specifically tailored for ultrasound images. In summary, our objective is to demonstrate the potential utility of neural style transfer in scenarios with limited datasets, particularly in the context of ultrasound images using the dataset of breast ultrasound images (Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy, 2020).
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