Neural style transfer in tiny sets of ultrasound images for data augmentation

A. M. Camargo; J. Olveres; B. Escalante-Ramírez

doi:10.1117/12.3017658

18 June 2024 Neural style transfer in tiny sets of ultrasound images for data augmentation

A. M. Camargo, J. Olveres, B. Escalante-Ramírez

Proceedings Volume 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII; 1299805 (2024) https://doi.org/10.1117/12.3017658
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

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).

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

A. M. Camargo, J. Olveres, and B. Escalante-Ramírez "Neural style transfer in tiny sets of ultrasound images for data augmentation", Proc. SPIE 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII, 1299805 (18 June 2024); https://doi.org/10.1117/12.3017658

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