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Modern deep learning algorithms geared towards clinical adaption rely on asignificant amount of high fidelity labeled data. Low-resource settings posechallenges like acquiring high fidelity data and becomes the bottleneck fordeveloping artificial intelligence applications. Ultrasound images, stored inDigital Imaging and Communication in Medicine (DICOM) format, have additionalmetadata data corresponding to ultrasound image parameters and medical exams.In this work, we leverage DICOM metadata from ultrasound images to help learnrepresentations of the ultrasound image. We demonstrate that the proposedmethod outperforms the non-metadata based approaches across differentdownstream tasks.

Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging