Self-Supervised Pretraining with DICOM Metadata in Ultrasound Imaging
Abstract
Modern deep learning algorithms geared towards clinical adaption usually rely on a large 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 approaches without using metadata across a variety of downstream tasks.
Cite
Text
Hu et al. "Self-Supervised Pretraining with DICOM Metadata in Ultrasound Imaging." Proceedings of the 5th Machine Learning for Healthcare Conference, 2020.Markdown
[Hu et al. "Self-Supervised Pretraining with DICOM Metadata in Ultrasound Imaging." Proceedings of the 5th Machine Learning for Healthcare Conference, 2020.](https://mlanthology.org/mlhc/2020/hu2020mlhc-selfsupervised/)BibTeX
@inproceedings{hu2020mlhc-selfsupervised,
title = {{Self-Supervised Pretraining with DICOM Metadata in Ultrasound Imaging}},
author = {Hu, Szu-Yen and Wang, Shuhang and Weng, Wei-Hung and Wang, JingChao and Wang, XiaoHong and Ozturk, Arinc and Li, Quan and Kumar, Viksit and Samir, Anthony E.},
booktitle = {Proceedings of the 5th Machine Learning for Healthcare Conference},
year = {2020},
pages = {732-749},
volume = {126},
url = {https://mlanthology.org/mlhc/2020/hu2020mlhc-selfsupervised/}
}