Abstract
We investigate the usefulness of formula-driven supervised learning (FDSL) for breast ultrasound (US) image analysis. Medical data are usually too scarce to develop a better performing deep learning model from scratch. Transfer learning with networks pre-trained on ImageNet is commonly applied to address this problem. FDSL techniques have been recently investigated as an alternative solution to ImageNet based approaches. In the FDSL setting, networks for transfer learning applications are developed using large amounts of synthetic images generated with mathematical formulas, possibly taking into account the characteristics of the target data. In this work, we use Field II to develop a large synthetic dataset of 100 000 US images presenting different contour objects, as shape features play an important role in breast mass characterization in US. Synthetic data are utilized to pre-train the ResNet50 classification model and various variants of the U-Net segmentation network. Next, the pre-trained models are fine-tuned on breast mass US images. Our results demonstrate that the proposed FDSL approach can provide good performance with respect to breast mass classification and segmentation.
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Acknowledgments
The authors do not have any conflicts of interest. This work was supported by the National Science Center of Poland (2019/35/B/ST7/03792), program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development AMED (JP15dm0207001) and the Japan Society for the Promotion of Science (JSPS, Fellowship PE21032).
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Byra, M., Klimonda, Z., Litniewski, J. (2023). Pre-training with Simulated Ultrasound Images for Breast Mass Segmentation and Classification. In: Bhattarai, B., et al. Data Engineering in Medical Imaging. DEMI 2023. Lecture Notes in Computer Science, vol 14314. Springer, Cham. https://doi.org/10.1007/978-3-031-44992-5_4
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