Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data. Low-resource settings pose challenges like acquiring high fidelity data and becomes the bottleneck for developing artificial intelligence applications. Ultrasound images, stored in Digital Imaging and Communication in Medicine (DICOM) format, have additional metadata data corresponding to ultrasound image parameters and medical exams. In this work, we leverage DICOM metadata from ultrasound images to help learn representations of the ultrasound image. We demonstrate that the proposed method outperforms the non-metadata based approaches across different downstream tasks.
@article{arxiv.2003.09070,
title = {Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging},
author = {Szu-Yeu Hu and Shuhang Wang and Wei-Hung Weng and JingChao Wang and XiaoHong Wang and Arinc Ozturk and Qian Li and Viksit Kumar and Anthony E. Samir},
journal= {arXiv preprint arXiv:2003.09070},
year = {2020}
}