Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging
This addresses the bottleneck of high-fidelity data acquisition for AI in low-resource clinical settings, though it is incremental as it builds on existing weakly supervised methods.
The paper tackled the problem of limited labeled data in ultrasound imaging by leveraging DICOM metadata to learn representations, resulting in improved performance over non-metadata approaches in downstream tasks.
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.