论文标题
在超声成像中使用DICOM元数据进行弱监督的上下文编码器
Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging
论文作者
论文摘要
旨在临床适应的现代深度学习算法依赖于大量高保真度标记的数据。低资源设置构成了挑战，例如获取高保真数据，并成为开发人工智能应用程序的瓶颈。超声图像存储在数字成像和通信中（DICOM）格式中，具有与超声图像参数和体检相对应的其他元数据数据。在这项工作中，我们利用超声图像中的DICOM元数据来帮助学习超声图像的表示。我们证明，所提出的方法在不同下游任务上优于基于非米达塔的方法。
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.
